Category Archives: UNL / Scope & Methods

Naming Names

Authoritative List of Core, New Core, and Gap countries,” by Thomas Barnett, Thomas P.M. Barnett :: Weblog, 16 April 2006, http://www.thomaspmbarnett.com/weblog/archives2/003162.html.

Before Redefining the Gap, and then Synthesizing that with Barnett’s own theory, I asked if there was a list of countries in the Core and the Gap

Tom responded:

Did once, but strictly by old-line and just Core-Gap.

Of course, the concept is meant to be dynamic and open to interpretation, not dogma.

So only thing that interests me is how breakdown pushes you to contemplate the how and why of shrinking the Gap.

Once you accept that challenge, you can do no serious wrong in my eyes.

And you can quote me on that.

Still, I was curious, and the data for my interpretation of his Core-Gap division, as well as the AfroIslamic Gap, is available as a computer file from this blog. A more human readable version of the Old Core, New Core, Gap division, plus the Synthesized division, is below.

I realize some of the divisions between the Old Core and New Core are arbitrary. There’s no authoritative list, so I did the best I could. Don’t like them? Then create your own list, or add your comments below.


The Old Core, The New Core, and the Gap

States of the Non-Integrating Gap

Afghanistan
Albania
Algeria
Angola
Anguilla
Antigua and Barbuda
Armenia
Azerbaijan
Bahamas, The
Bahrain
Bangladesh
Barbados
Belarus
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Brunei
Bulgaria
Burkina Faso
Burma
Burundi
Cambodia
Cameroon
Cape Verde
Central African Republic
Chad
Colombia
Comoros
Congo Dem
Congo Rep
Cook Islands
Costa Rica
Cote d’Ivoire
Croatia
Cuba
Cyprus
Djibouti
Dominica
Dominican Republic
East Timor
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Ethiopia
Fiji
Gabon
Gambia
Georgia
Ghana
Grenada
Guam
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Indonesia
Iran
Iraq
Israel
Jamaica
Jordan
Kazakhstan
Kenya
Kiribati
Korea DPRK
Kuwait
Kyrgyzstan
Laos
Lebanon
Lesotho
Liberia
Libya
Macedonia
Madagascar
Malawi
Malaysia
Maldives
Mali
Marshall Islands
Martinique
Mauritania
Mauritius
Mayotte
Mexico
Micronesia
Moldova
Monaco
Montserrat
Morocco
Mozambique
Namibia
Nauru
Nepal
New Caledonia
Nicaragua
Niger
Nigeria
Niue
Norfolk Island
Northern Marianas
Oman
Pakistan
Palau
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Puerto Rico
Qatar
Reunion
Romania
Rwanda
Saint Helena
Saint Kitts and Nevis
Saint Lucia
Saint Pierre and Miquelon
Saint Vincent and the Grenadines
Samoa
Sao Tome
Saudi Arabia
Senegal
Serbia and Montenegro
Seychelles
Sierra Leone
Singapore
Solomon Islands
Somalia
Sri Lanka
Sudan
Suriname
Swaziland
Syria
Tajikistan
Tanzania
Thailand
Togo
Tokelau
Tonga
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Turks and Caicos Islands
Tuvalu
Uganda
Ukraine
United Arab Emirates
Uzbekistan
Vanuatu
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe

States of the New Core

Argentina
Australia
Bermuda
Brazil
Chile
China
Czech Republic
Estonia
Hungary
India
Korea ROK
Latvia
Lithuania
Mongolia
Poland
Russia
Slovakia
Slovenia
South Africa
Taiwan
Uruguay

States of the Old Core

Andorra
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Holy See (Vatican City)
Iceland
Ireland
Italy
Japan
Liechtenstein
Luxembourg
Malta
Netherlands
New Zealand
Norway
Portugal
San Marino
Spain
Sweden
Switzerland
United Kingdom
United States

The Afro-Islamic Gap, The Seam, the New Core, and the Old Core

States of the Afro-Islamic Gap

Afghanistan
Albania
Algeria
Angola
Azerbaijan
Bahrain
Bangladesh
Benin
Botswana
Brunei
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo Dem
Congo Rep
Cote d’Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Guyana
Indonesia
Iran
Iraq
Jordan
Kazakhstan
Kenya
Kuwait
Kyrgyzstan
Lebanon
Lesotho
Liberia
Libya
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Oman
Pakistan
Qatar
Rwanda
Sao Tome
Saudi Arabia
Senegal
Seychelles
Sierra Leone
Somalia
Sudan
Suriname
Swaziland
Syria
Tajikistan
Tanzania
Togo
Tunisia
Turkey
Turkmenistan
Uganda
United Arab Emirates
Uzbekistan
Yemen
Zambia
Zimbabwe

States of the Seam

Anguilla
Antigua and Barbuda
Armenia
Bahamas, The
Barbados
Belarus
Belize
Bhutan
Bolivia
Bosnia and Herzegovina
Bulgaria
Burma
Cambodia
Colombia
Cook Islands
Costa Rica
Croatia
Cuba
Cyprus
Dominica
Dominican Republic
East Timor
Ecuador
El Salvador
Fiji
Georgia
Grenada
Guam
Guatemala
Haiti
Honduras
Israel
Jamaica
Kiribati
Korea DPRK
Laos
Macedonia
Marshall Islands
Martinique
Mayotte
Mexico
Micronesia
Moldova
Monaco
Montserrat
Nauru
Nepal
New Caledonia
Nicaragua
Niue
Norfolk Island
Northern Marianas
Palau
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Puerto Rico
Reunion
Romania
Saint Helena
Saint Kitts and Nevis
Saint Lucia
Saint Pierre and Miquelon
Saint Vincent and the Grenadines
Samoa
Serbia and Montenegro
Singapore
Solomon Islands
South Africa
Sri Lanka
Thailand
Tokelau
Tonga
Trinidad and Tobago
Turks and Caicos Islands
Tuvalu
Ukraine
Vanuatu
Venezuela
Vietnam

States of the New Core

Argentina
Australia
Bermuda
Brazil
Chile
China
Czech Republic
Estonia
Hungary
India
Korea ROK
Latvia
Lithuania
Mongolia
Poland
Russia
Slovakia
Slovenia
Taiwan
Uruguay

States of the Old Core

Andorra
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Holy See (Vatican City)
Iceland
Ireland
Italy
Japan
Liechtenstein
Luxembourg
Malta
Netherlands
New Zealand
Norway
Portugal
San Marino
Spain
Sweden
Switzerland
United Kingdom
United States

Synthesizing the Gap: Convergent Thinking and Mapping Our World

In my study, Redefining the Gap, I contrasted Tom Barnett’s model of an Old Core – New Core – Gap model with a rival world of an AfroIslamic – Not-AfroIslamic, and found my alternative to be generally better. However, with ZenPundit and Enterprise Resilience Blog talking about horizontal thinking, I decided to apply something similar to my Redefinition of the Gap. The results are here, and discussion is below the fold:

Nation Brutal Nasty Poor Solitary Short IV
OCNCG -0.164 0.469 0.733 0.6441 0.43 0.655
AfroIslam 0.049 0.595 0.337 0.3142 0.63 0.667
SyntheticTPMB -0.089 0.622 0.676 0.5994 0.60 0.781
SyntheticXP -0.092 0.614 0.675 0.6024 0.62 0.784
SyntheticBoth -0.091 0.619 0.677 0.6023 0.61 0.785

Best=Green; Good=Blue; Fair=Yellow; Acceptable=White; Worst=Red


Tom Barnett’s model carefully delineated the Old Core from the New Core, while mine zeroed in on the worst of the Gap. One might draw a simple matrix of the focus of our models, of which mine was slightly better overall in measuring predicting Hobbesian conditions.

I thus created two Synthesis — one a “SynthesisTPMB” which completely adopted Barnett’s list of Old Core and New Core countries, but then separated his “Gap” into the AfroIslamic Gaps and the “Seam” (countries he labeled as Gap but which were neither African nor Islamic). A second Synthesis, “SynthesisXP,” accepted by definition of the Gap as all African or Islamic states, the Seam as all remaining Barnettian Gap states as Seam, and the rest of the world either New Core or Old Core, depending on Barnett’s breakdown.

One can visualize these syntheses as a coming together process:

tdaxp_tpmb_analogy_0

Comparing these two new syntheses with the original models studied (Barnett’s “OCNCG” model of the Old Core, New Core, and Gap, with my model of an AfroIslamic Gap and an “everybody else” Core), only one of the synthesized models (the one closer to Barnett) was “best” at any of the Hobbesian variables used. It had a positive correlation of .622 with Nastiness, and the synthesis closer to mine was second with .614. But interestingly, both synthesize were better than either Barnett’s Gap model or my Gap model overall. This is because that, while his and mine were better at some specifics, we were also were on other specifics.

Last, I synthesized the syntheses, and created the best solution.

tdaxp_tpmb_analogy_1

In practical terms, the only difference Between the two earlier syntheses was how to treat South Africa. Barnett defines the Republic as New Core, thus rating a “2” on the 3-0 scale used, 3 being the best. However, I defined the RSA as part of the AfroIslamic Gap, thus rating only a “1.” A reasonable compromise would be to define any state that’s “Core” in one model and “Gap” in the other as part of the Seam, thus cutting the difference and giving Suid-Afrika a score of “2.”

This final synthesis shines bright. In every Hobbesian measure it is 2nd or 3rd most predictive, but overall it is the first, with a correlation of .785.

Redefining the Gap 14, Appendix: National Codes

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

The CSV output file. Boring, I know. But makes it easier to run your own tests if you disagree.


Nation;Brutal;Nasty;Poor;Solitary;Short;IV;OCNCG;CG;G77;G2277;AfroIslam;Nalign;G15Nalign;LDCs;LDCsLLDCs;FreeComNon
Brutal
Nasty
Poor
Solitary
Short
IV
OCNCG
CG
G77
G2277
AfroIslam
Nalign
G15Nalign
LDCs
LLDCsLDCs
FreeComNon

Afghanistan;0.833333333333333;0.25;0.00575539568345324;3.74260418305409e-006;0.210650422479859;calculate IV;0;0;0;0;0;0;0;0;0;0
Albania;0.916666666666667;0.666666666666667;0.0647482014388489;0.000319828995244291;0.8805266260562;calculate IV;0;0;1;2;0;1;2;1;2;1
Algeria;1;0.25;0.097841726618705;5.4571350582082e-005;0.798585183729613;calculate IV;0;0;0;1;0;0;1;0;1;1
Andorra;1;1;0.339568345323741;0.151605629229859;1;calculate IV;2;1;1;2;1;1;2;1;2;2
Angola;0.916666666666667;0.25;0.0402877697841727;0.000315537624467798;0.117901355865592;calculate IV;0;0;0;0;0;0;0;0;1;0
Anguilla;1;1;0.102158273381295;0.044825344270446;0.87757909215956;calculate IV;0;0;1;2;1;1;2;0;1;
Antigua and Barbuda;1;0.833333333333333;0.152517985611511;0.0474256850595396;0.776969935154254;calculate IV;0;0;0;0;1;1;2;0;1;
Argentina;1;0.833333333333333;0.19136690647482;0.0472426232374024;0.854784830025545;calculate IV;1;1;0;1;1;1;2;0;1;1
Armenia;1;0.416666666666667;0.0705035971223022;0.00454856035180742;0.770681862841423;calculate IV;0;0;1;2;1;1;2;1;2;1
Australia;0.916666666666667;1;0.454676258992806;0.403904100952972;0.940852819807428;calculate IV;1;1;1;2;1;1;2;1;2;2
Austria;1;1;0.467625899280576;0.33839763206548;0.912752996659461;calculate IV;2;1;1;2;1;1;2;1;2;2
Azerbaijan;0.916666666666667;0.25;0.0618705035971223;8.8364170996729e-005;0.613676557280409;calculate IV;0;0;1;2;0;1;2;1;2;1
Bahamas, The;1;1;0.266187050359712;0.00180746265924107;0.648064452741206;calculate IV;0;0;0;0;1;0;0;0;1;
Bahrain;1;0.333333333333333;0.289208633093525;0.00427346669414268;0.821968952642955;calculate IV;0;0;0;0;0;0;0;0;1;0
Bangladesh;1;0.5;0.0244604316546763;2.7606161053281e-006;0.586362743171546;calculate IV;0;0;0;0;0;0;0;0;0;0
Barbados;1;1;0.244604316546763;0.00131678627659656;0.789349577520142;calculate IV;0;0;0;0;1;0;0;0;1;0
Belarus;1;0.0833333333333334;0.105035971223022;0.00311629014622457;0.71644723914325;calculate IV;0;0;1;2;1;0;0;1;2;1
Belgium;0.916666666666667;1;0.453237410071942;0.329910507131293;0.906857928866182;calculate IV;2;1;1;2;1;1;2;1;2;2
Belize;1;0.916666666666667;0.0920863309352518;0.0204429657336308;0.701120062880723;calculate IV;0;0;0;0;1;0;0;0;1;0
Benin;1;0.833333333333333;0.0100719424460432;0.000158328460428524;0.401257614462566;calculate IV;0;0;0;0;0;0;0;0;0;0
Bermuda;1;1;1;0.285681834737974;0.890941245824327;calculate IV;1;1;1;2;1;1;2;1;2;
Bhutan;1;0.25;0.0143884892086331;2.01260682164534e-006;0.435449007663588;calculate IV;0;0;0;0;1;0;0;0;0;0
Bolivia;1;0.666666666666667;0.0330935251798561;0.00272989463520175;0.65278050697583;calculate IV;0;0;0;0;1;0;0;0;1;0
Bosnia and Herzegovina;1;0.583333333333333;0.0920863309352518;0.00289801399046188;0.891727254863431;calculate IV;0;0;0;0;1;1;2;1;2;1
Botswana;1;0.833333333333333;0.138129496402878;0.00151183069765953;0.0220082530949107;calculate IV;0;0;0;0;0;0;0;0;0;0
Brazil;1;0.75;0.115107913669065;0.0357200531493069;0.77323639221851;calculate IV;1;1;0;1;1;1;2;0;1;0
Brunei;1;0.25;0.333812949640288;0.000108826795393671;0.832973079190411;calculate IV;0;0;0;0;0;0;0;0;1;1
Bulgaria;1;0.916666666666667;0.123741007194245;0.0197846502011744;0.779720966791118;calculate IV;0;0;1;2;1;1;2;1;2;1
Burkina Faso;1;0.416666666666667;0.0115107913669065;4.10318359952257e-005;0.318923167616428;calculate IV;0;0;0;0;0;0;0;0;0;1
Burma;0.916666666666667;0;0.0172661870503597;1.3879346935283e-006;0.557083906464924;calculate IV;0;0;0;0;1;0;0;0;0;0
Burundi;1;0.333333333333333;0.00287769784172662;2.930213687102e-005;0.357437610532521;calculate IV;0;0;0;0;0;0;0;0;0;1
Cambodia;1;0.25;0.0258992805755396;0.000144880990274733;0.524071526822558;calculate IV;0;0;0;0;1;0;0;0;1;1
Cameroon;0.916666666666667;0.166666666666667;0.0215827338129496;2.99869301875054e-006;0.364315189624681;calculate IV;0;0;0;0;0;0;0;0;1;1
Canada;0.916666666666667;1;0.467625899280576;0.162897042870183;0.9353507565337;calculate IV;2;1;1;2;1;1;2;1;2;2
Cape Verde;1;1;0.0834532374100719;0.000563107137961415;0.748870112006288;calculate IV;0;0;0;0;0;0;0;0;0;0
Central African Republic;1;0.25;0.0100719424460432;4.62014753108993e-006;0.214580467675378;calculate IV;0;0;0;0;0;0;0;0;0;0
Chad;0.916666666666667;0.25;0.0201438848920863;1.07658397856977e-006;0.292788367066221;calculate IV;0;0;0;0;0;0;0;0;0;0
Chile;1;1;0.156834532374101;0.0317975121349218;0.867557476910984;calculate IV;1;1;0;0;1;0;1;0;1;0
China;0.916666666666667;0.0833333333333334;0.0848920863309353;0.000218249315914352;0.785223030064846;calculate IV;1;1;0;1;1;1;2;0;1;0
Colombia;1;0.5;0.0964028776978417;0.0135636240448688;0.773629396738062;calculate IV;0;0;0;1;1;0;1;0;1;0
Comoros;1;0.5;0.00287769784172662;1.32808433087925e-005;0.583808213794459;calculate IV;0;0;0;0;0;0;0;0;0;0
Congo Dem;0.833333333333333;0.166666666666667;0.00575539568345324;4.58863386799357e-006;0.37021025741796;calculate IV;0;0;0;1;0;0;0;0;1;0
Congo Rep;1;0.416666666666667;0.00431654676258993;1.90022203067273e-005;0.396541560227943;calculate IV;0;0;0;0;0;0;0;0;1;0
Cook Islands;1;1;0.0661870503597122;0.0387568861854605;;calculate IV;0;0;1;2;1;1;2;0;1;0
Costa Rica;1;1;0.139568345323741;0.004720368771354;0.872470033405384;calculate IV;0;0;0;0;1;1;2;0;1;0
Cote d’Ivoire;1;0.166666666666667;0.0158273381294964;0.000174554462920693;0.3183336608371;calculate IV;0;0;0;1;0;0;0;0;1;0
Croatia;1;0.833333333333333;0.161151079136691;0.00659054790531347;0.826488504617803;calculate IV;0;0;1;2;1;1;2;1;2;1
Cuba;1;0;0.041726618705036;0.000257702422466492;0.880133621536648;calculate IV;0;0;0;0;1;0;0;0;1;1
Cyprus;0.916666666666667;1;0.0969064748201439;0.0913833521351755;0.888190214187463;calculate IV;0;0;1;2;1;0;0;0;1;2
Czech Republic;1;1;0.254676258992806;0.122491576322979;0.856749852623305;calculate IV;1;1;1;2;1;1;2;1;2;1
Denmark;1;1;0.474820143884892;0.592043287082336;0.887600707408135;calculate IV;2;1;1;2;1;1;2;1;2;2
Djibouti;1;0.333333333333333;0.0129496402877698;0.00159059883049244;0.207309884063667;calculate IV;0;0;0;0;0;0;0;0;0;0
Dominica;1;1;0.0733812949640288;0.00989856810004718;0.830222047553547;calculate IV;0;0;0;0;1;1;2;0;1;0
Dominican Republic;1;0.833333333333333;0.0892086330935252;0.0135883949793045;0.768520337983887;calculate IV;0;0;0;0;1;0;0;0;1;0
East Timor;1;0.666666666666667;0;0.000309396986439389;0.661033601886422;calculate IV;0;0;0;0;1;1;2;1;2;0
Ecuador;0.916666666666667;0.666666666666667;0.0503597122302158;0.00183075741976403;0.860679897818825;calculate IV;0;0;0;0;1;0;0;0;1;0
Egypt;1;0.25;0.0575539568345324;3.29969533777493e-005;0.759874238553743;calculate IV;0;0;0;1;0;0;1;0;1;0
El Salvador;1;0.75;0.0676258992805755;0.000987259447942095;0.763804283749263;calculate IV;0;0;0;0;1;1;2;0;1;0
Equatorial Guinea;1;0.0833333333333334;0.716546762589928;5.09695568256976e-005;0.332481823540971;calculate IV;0;0;0;0;0;0;0;0;0;0
Eritrea;0.833333333333333;0.0833333333333334;0.00863309352517986;0.000334505732695792;0.518962468068383;calculate IV;0;0;0;0;0;0;0;0;0;0
Estonia;1;1;0.23021582733813;0.0582502298465326;0.774611908036942;calculate IV;1;1;1;2;1;1;2;1;2;1
Ethiopia;0.916666666666667;0.333333333333333;0.00575539568345324;1.77936598444122e-006;0.322460208292395;calculate IV;0;0;0;1;0;0;0;0;0;0
Fiji;1;0.583333333333333;0.0820143884892086;0.00290702765736732;0.730988406366673;calculate IV;0;0;0;0;1;1;2;0;1;0
Finland;1;1;0.434532374100719;0.439688461565612;0.90155236785223;calculate IV;2;1;1;2;1;1;2;1;2;2
France;0.916666666666667;1;0.42589928057554;0.0734106119922448;0.925722145804677;calculate IV;2;1;1;2;1;1;2;1;2;2
Gabon;1;0.416666666666667;0.0776978417266187;0.000332732516856352;0.429750442130085;calculate IV;0;0;0;1;0;0;0;0;1;0
Gambia;1;0.5;0.0201438848920863;1.21117054216595e-005;0.422872863037925;calculate IV;0;0;0;0;0;0;0;0;0;0
Georgia;0.916666666666667;0.583333333333333;0.041726618705036;0.00293380509836003;0.854195323246217;calculate IV;0;0;1;2;1;1;2;1;2;1
Germany;1;1;0.423021582733813;0.142083248439128;0.90744743564551;calculate IV;2;1;1;2;1;1;2;1;2;2
Ghana;1;0.833333333333333;0.0287769784172662;2.6207007248577e-005;0.515818431911967;calculate IV;0;0;0;1;0;0;0;0;1;0
Greece;0.916666666666667;0.916666666666667;0.322302158273381;0.0593444532851753;0.916093535075653;calculate IV;2;1;1;2;1;1;2;1;2;2
Grenada;1;0.916666666666667;0.0661870503597122;0.000306891813736115;0.633719787777559;calculate IV;0;0;0;0;1;0;0;0;1;0
Guam;1;1;0.210071942446043;0.000849569688786761;0.903124385930438;calculate IV;0;0;1;2;1;1;2;0;1;0
Guatemala;1;0.5;0.0690647482014388;0.00502663953061769;0.72234230693653;calculate IV;0;0;0;1;1;0;0;0;1;0
Guinea;1;0.25;0.0258992805755396;5.7449654792886e-005;0.331695814501867;calculate IV;0;0;0;0;0;0;0;0;0;0
Guinea-Bissau;1;0.5;0.00575539568345324;5.30292856022288e-006;0.280015720180782;calculate IV;0;0;0;0;0;0;0;0;0;0
Guyana;1;0.833333333333333;0.0489208633093525;0.00182193087388134;0.653173511495382;calculate IV;0;0;0;0;0;0;0;0;1;0
Haiti;0.916666666666667;0.0833333333333334;0.0172661870503597;5.52227700830592e-007;0.40499115739831;calculate IV;0;0;0;0;1;1;2;0;0;0
Holy See (Vatican City);1;1;;0.0590753570119543;;calculate IV;2;1;1;2;1;1;2;1;2;2
Honduras;1;0.666666666666667;0.0345323741007194;0.000994269302805864;0.72135979563765;calculate IV;0;0;0;0;1;0;0;0;1;0
Hungary;1;1;0.22589928057554;0.0400369158635287;0.786795048143053;calculate IV;1;1;1;2;1;1;2;1;2;1
Iceland;1;1;0.496402877697842;0.971312252203734;0.937119276871684;calculate IV;2;1;1;2;1;1;2;1;2;2
India;0.666666666666667;0.75;0.0431654676258993;0.00109961419760908;0.630575751621143;calculate IV;1;1;0;1;1;0;1;0;1;0
Indonesia;0.916666666666667;0.583333333333333;0.0474820143884892;0.000839522442599572;0.731970917665553;calculate IV;0;0;0;0;0;0;1;0;1;0
Iran;0.916666666666667;0.166666666666667;0.110791366906475;0.000116805809586501;0.739634505796817;calculate IV;0;0;0;1;0;0;1;0;1;0
Iraq;0.583333333333333;0.166666666666667;0.0431654676258993;2.28409585712742e-007;0.715071723324818;calculate IV;0;0;0;0;0;0;0;0;1;0
Ireland;1;1;0.484892086330935;0.0898706862880126;0.886421693849479;calculate IV;2;1;1;2;1;1;2;1;2;2
Israel;0.75;0.833333333333333;0.315107913669065;0.257403668722424;0.920416584790725;calculate IV;0;0;1;2;1;1;2;1;2;2
Italy;0.916666666666667;1;0.402877697841727;0.0327868312198717;0.927294163882885;calculate IV;2;1;1;2;1;1;2;1;2;2
Jamaica;1;0.75;0.0546762589928058;0.000704776686842666;0.798192179210061;calculate IV;0;0;0;0;1;0;1;0;1;0
Japan;1;0.916666666666667;0.435971223021583;0.255623428084247;0.955590489290627;calculate IV;2;1;1;2;1;1;2;1;2;2
Jordan;1;0.416666666666667;0.0633093525179856;0.000723171624212719;0.89958734525447;calculate IV;0;0;0;0;0;0;0;0;1;0
Kazakhstan;1;0.25;0.120863309352518;0.00204080098472499;0.673413244252309;calculate IV;0;0;1;2;0;1;2;1;2;1
Kenya;1;0.666666666666667;0.0115107913669065;0.000513135380841281;0.320495185694635;calculate IV;0;0;0;0;0;0;1;0;1;0
Kiribati;1;1;0.00575539568345324;0.000522215577198017;0.578895657300059;calculate IV;0;0;1;2;1;1;2;0;0;0
Korea DPRK;0.75;0;0.0201438848920863;;0.766948319905679;calculate IV;0;0;0;0;1;0;0;0;1;1
Korea ROK;0.75;0.916666666666667;0.287769784172662;0.170125881668889;0.872863037924936;calculate IV;1;1;1;2;1;1;2;0;1;2
Kuwait;0.916666666666667;0.416666666666667;0.322302158273381;0.00154242725140448;0.876007074081352;calculate IV;0;0;0;0;0;0;0;0;1;0
Kyrgyzstan;1;0.25;0.0201438848920863;0.00543805430471483;0.704853605816467;calculate IV;0;0;1;2;0;1;2;1;2;1
Laos;1;0.0833333333333334;0.0215827338129496;0.000276653639623723;0.449400668107683;calculate IV;0;0;0;0;1;0;0;0;0;0
Latvia;1;0.916666666666667;0.181294964028777;0.0358027778984147;0.760660247592847;calculate IV;1;1;1;2;1;1;2;1;2;1
Lebanon;0.833333333333333;0.25;0.0705035971223022;0.00132843287138317;0.791118097858125;calculate IV;0;0;0;1;0;0;0;0;1;0
Lesotho;1;0.75;0.037410071942446;0.000116463073788665;0.0349774022401258;calculate IV;0;0;0;0;0;0;0;0;0;0
Liberia;1;0.416666666666667;0.00719424460431655;2.51379576383517e-006;0.138141088622519;calculate IV;0;0;0;0;0;0;0;0;1;0
Libya;1;0;0.115107913669065;1.21817621318283e-005;0.865985458832776;calculate IV;0;0;0;0;0;0;0;0;1;0
Liechtenstein;1;1;0.353956834532374;0.337090669814066;0.924739634505797;calculate IV;2;1;1;2;1;1;2;1;2;2
Lithuania;1;0.833333333333333;0.194244604316547;0.0581518140472541;0.817056396148556;calculate IV;1;1;1;2;1;1;2;1;2;1
Luxembourg;1;1;0.794244604316547;0.227162501032372;0.909215955983494;calculate IV;2;1;1;2;1;1;2;1;2;2
Macedonia;1;0.666666666666667;0.103597122302158;0.00264103698202664;0.812536844173708;calculate IV;0;0;1;2;1;1;2;1;2;1
Madagascar;1;0.666666666666667;0.00719424460431655;6.62077014444708e-005;0.485753586166241;calculate IV;0;0;0;0;0;0;0;0;1;0
Malawi;1;0.5;0.00287769784172662;3.58435711786703e-005;0.178424051876597;calculate IV;0;0;0;0;0;0;0;0;0;0
Malaysia;1;0.5;0.143884892086331;0.00948517882398848;0.783651011986638;calculate IV;0;0;0;0;0;0;1;0;1;0
Maldives;1;0.25;0.0503597122302158;0.00572125958221411;0.624680683827864;calculate IV;0;0;0;0;0;0;0;0;0;0
Mali;1;0.833333333333333;0.00863309352517986;3.5243046178583e-005;0.321870701513067;calculate IV;0;0;0;0;0;0;0;0;0;0
Malta;1;1;0.267625899280576;0.0410384861698077;0.911573983100806;calculate IV;2;1;1;2;1;0;0;1;2;
Marshall Islands;1;1;0.0172661870503597;0.000151871373382602;0.740617017095697;calculate IV;0;0;0;0;1;1;2;0;1;
Martinique;1;1;0.201438848920863;0.000245471371589671;0.914914521516997;calculate IV;0;0;1;2;1;1;2;0;1;0
Mauritania;1;0.25;0.023021582733813;1.01080832522917e-005;0.402829632540774;calculate IV;0;0;0;0;0;0;0;0;0;0
Mauritius;1;1;0.184172661870504;0.0061061087343336;0.786205541363726;calculate IV;0;0;0;0;0;0;0;0;1;
Mayotte;1;1;0.0316546762589928;7.60008424895359e-006;0.572607584987227;calculate IV;0;0;1;2;1;1;2;0;1;0
Mexico;1;0.833333333333333;0.139568345323741;0.0288463173193589;0.84083316958145;calculate IV;0;0;1;2;1;1;2;1;2;0
Micronesia;1;1;0.023021582733813;0.00613151538994343;0.735507958341521;calculate IV;0;0;0;0;1;1;2;0;1;0
Moldova;1;0.583333333333333;0.0244604316546763;0.0105667733699509;0.649046964040087;calculate IV;0;0;1;2;1;1;2;1;2;1
Monaco;1;0.916666666666667;0.38273381294964;0.0336492355740962;0.924936136765573;calculate IV;0;0;1;2;1;1;2;1;2;2
Mongolia;1;0.833333333333333;0.0258992805755396;0.000103679619945581;0.634112792297111;calculate IV;1;1;0;0;1;0;0;0;1;1
Montserrat;1;1;0.0431654676258993;0.0588166663219277;0.90842994694439;calculate IV;0;0;1;2;1;1;2;0;1;
Morocco;0.916666666666667;0.416666666666667;0.0561151079136691;0.000116770709792534;0.752996659461584;calculate IV;0;0;0;0;0;0;0;0;1;0
Mozambique;1;0.583333333333333;0.0129496402877698;0.000561525248739346;0.141481627038711;calculate IV;0;0;0;0;0;0;0;0;0;0
Namibia;0.916666666666667;0.75;0.112230215827338;0.00244880186837669;0.211632933778738;calculate IV;0;0;0;0;0;0;0;0;1;0
Nauru;1;1;0.0661870503597122;0.00598544128811652;0.598545883277658;calculate IV;0;0;1;2;1;1;2;0;1;0
Nepal;1;0.333333333333333;0.0158273381294964;0.000424208066849347;0.541560227942621;calculate IV;0;0;0;0;1;0;0;0;0;0
Netherlands;0.916666666666667;1;0.434532374100719;0.628928196417423;0.910591471801926;calculate IV;2;1;1;2;1;1;2;1;2;2
New Caledonia;1;1;0.210071942446043;0.0471836666246662;0.818431911966988;calculate IV;0;0;1;2;1;1;2;0;1;
New Zealand;1;1;0.342446043165468;0.282050063522977;0.907643937905286;calculate IV;2;1;1;2;1;1;2;1;2;2
Nicaragua;1;0.666666666666667;0.0287769784172662;0.0034672813730534;0.746905089408528;calculate IV;0;0;0;0;1;0;0;0;1;0
Niger;1;0.666666666666667;0.00575539568345324;1.67285900979496e-005;0.21890351739045;calculate IV;0;0;0;0;0;0;0;0;0;0
Nigeria;0.916666666666667;0.5;0.00863309352517986;1.78039333935262e-005;0.284142267636078;calculate IV;0;0;0;1;0;0;1;0;1;0
Niue;1;1;0.0460431654676259;;;calculate IV;0;0;1;2;1;1;2;0;1;
Norfolk Island;1;1;;0.0761351078728705;;calculate IV;0;0;1;2;1;1;2;0;1;
Northern Marianas;1;1;0.17410071942446;0.000370946859349235;0.854195323246217;calculate IV;0;0;1;2;1;1;2;0;1;
Norway;1;1;0.60431654676259;0.445358671871538;0.921988602868933;calculate IV;2;1;1;2;1;1;2;1;2;2
Oman;1;0.25;0.18705035971223;0.00160766927541183;0.800746708587149;calculate IV;0;0;0;0;0;0;0;0;1;0
Pakistan;0.583333333333333;0.25;0.0287769784172662;0.000353367603246632;0.604637453330713;calculate IV;0;0;0;1;0;0;0;0;1;0
Palau;1;1;0.123741007194245;0.000222954762683404;0.742778541953232;calculate IV;0;0;0;0;1;1;2;0;1;0
Panama;1;0.916666666666667;0.0964028776978417;0.00336088295023979;0.837099626645706;calculate IV;0;0;0;0;1;0;0;0;1;0
Papua New Guinea;1;0.666666666666667;0.0287769784172662;0.000245165256906977;0.641776380428375;calculate IV;0;0;0;0;1;0;0;0;1;0
Paraguay;1;0.666666666666667;0.0647482014388489;0.00239900028347388;0.834741599528394;calculate IV;0;0;0;0;1;1;2;0;1;0
Peru;0.916666666666667;0.75;0.0820143884892086;0.0111063878417032;0.731381410886225;calculate IV;0;0;0;1;1;0;1;0;1;0
Philippines;0.916666666666667;0.75;0.0676258992805755;0.00164959018715851;0.738651994497937;calculate IV;0;0;0;1;1;0;0;0;1;0
Poland;1;1;0.176978417266187;0.0145609155871383;0.832187070151307;calculate IV;1;1;1;2;1;1;2;1;2;1
Portugal;0.916666666666667;1;0.261870503597122;0.121992621196446;0.885832187070151;calculate IV;2;1;1;2;1;1;2;1;2;2
Puerto Rico;1;1;0.260431654676259;5.14057862000796e-005;0.89958734525447;calculate IV;0;0;1;2;1;1;2;0;1;0
Qatar;1;0.25;0.369784172661871;0.0003523956408257;0.811161328355276;calculate IV;0;0;0;0;0;0;0;0;1;0
Reunion;1;1;0.0834532374100719;5.63145839159555e-005;0.816663391629004;calculate IV;0;0;1;2;1;1;2;0;1;0
Romania;1;0.75;0.115107913669065;0.00385291392764371;0.766555315386127;calculate IV;0;0;0;0;1;1;2;1;2;1
Russia;0.916666666666667;0.25;0.148201438848921;0.013982741163937;0.677146787188053;calculate IV;1;1;1;2;1;1;2;1;2;1
Rwanda;0.833333333333333;0.25;0.0129496402877698;0.00028082911379984;0.28846531735115;calculate IV;0;0;0;0;0;0;0;0;0;0
Saint Helena;1;1;0.0302158273381295;;0.890351739044999;calculate IV;0;0;1;2;1;1;2;0;1;
Saint Kitts and Nevis;1;0.916666666666667;0.120863309352518;0.00187612709193152;0.781685989388878;calculate IV;0;0;0;0;1;1;2;0;1;
Saint Lucia;1;0.916666666666667;0.0719424460431655;0.000226969831316104;0.80998231479662;calculate IV;0;0;0;0;1;0;0;0;1;
Saint Pierre and Miquelon;1;1;0.0949640287769784;0;0.903713892709766;calculate IV;0;0;1;2;1;1;2;0;1;
Saint Vincent and the Grenadines;1;0.916666666666667;0.0359712230215827;0.000272531586695001;0.810178817056396;calculate IV;0;0;0;0;1;1;2;0;1;
Samoa;1;0.833333333333333;0.0748201438848921;0.0792759818319325;0.75417567302024;calculate IV;0;0;0;0;1;1;2;0;0;0
San Marino;1;1;0.492086330935252;0.113249814920208;0.964629593240322;calculate IV;2;1;1;2;1;1;2;1;2;2
Sao Tome;1;0.833333333333333;0.0115107913669065;0.00807346277749045;0.6816663391629;calculate IV;0;0;0;0;0;0;0;0;0;
Saudi Arabia;0.916666666666667;0;0.179856115107914;0.000584991056389378;0.845942228335626;calculate IV;0;0;0;0;0;0;0;0;1;0
Senegal;1;0.75;0.018705035971223;7.25967441461536e-005;0.523285517783454;calculate IV;0;0;0;0;0;0;1;0;1;0
Serbia and Montenegro;1;0.75;0.0330935251798561;0.0031125695733421;0.831794065631755;calculate IV;0;0;1;2;1;0;0;1;2;1
Seychelles;1;0.666666666666667;0.106474820143885;0.0098469795651367;0.775397917076046;calculate IV;0;0;0;0;0;0;0;0;1;0
Sierra Leone;1;0.583333333333333;0.00719424460431655;7.05453583097852e-005;0.14934171742975;calculate IV;0;0;0;0;0;0;0;0;0;0
Singapore;1;0.416666666666667;0.424460431654676;0.231297661940152;0.964629593240322;calculate IV;0;0;0;0;1;0;0;0;1;0
Slovakia;1;1;0.22158273381295;0.0382362334472818;0.827471015916683;calculate IV;1;1;1;2;1;1;2;1;2;1
Slovenia;1;1;0.296402877697842;0.0449534192123646;0.858911377480841;calculate IV;1;1;1;2;1;1;2;1;2;1
Solomon Islands;1;0.666666666666667;0.018705035971223;0.00204865082014254;0.791707604637453;calculate IV;0;0;0;0;1;1;2;0;1;
Somalia;1;0.0833333333333334;0.00287769784172662;3.45105953028967e-007;0.31145608174494;calculate IV;0;0;0;0;0;0;0;0;0;0
South Africa;1;0.916666666666667;0.168345323741007;0.0159410352010711;0.198663784633523;calculate IV;1;1;0;1;0;0;0;1;2;2
Spain;0.833333333333333;1;0.356834532374101;0.0522649945278463;0.924150127726469;calculate IV;2;1;1;2;1;1;2;1;2;2
Sri Lanka;1;0.666666666666667;0.0561151079136691;0.000455666978849396;0.801532717626253;calculate IV;0;0;0;1;1;0;1;0;1;0
Sudan;0.916666666666667;0;0.0244604316546763;3.70884987462751e-008;0.516800943210847;calculate IV;0;0;0;0;0;0;0;0;0;0
Suriname;1;0.916666666666667;0.0532374100719424;0.000424912342628455;0.715071723324818;calculate IV;0;0;0;0;0;0;0;0;1;0
Swaziland;1;0.166666666666667;0.0733812949640288;0.0032315131329021;0;calculate IV;0;0;0;0;0;0;0;0;1;0
Sweden;1;1;0.423021582733813;0.458221068657248;0.941049322067204;calculate IV;2;1;1;2;1;1;2;1;2;2
Switzerland;1;1;0.502158273381295;0.370564930877604;0.941049322067204;calculate IV;2;1;1;2;1;1;2;1;2;2
Syria;0.833333333333333;0;0.0431654676258993;5.18401733011997e-006;0.740813519355472;calculate IV;0;0;0;1;0;0;0;0;1;0
Taiwan;1;0.916666666666667;0.37841726618705;0.254835169103505;0.8805266260562;calculate IV;1;1;1;2;1;1;2;0;1;2
Tajikistan;1;0.25;0.0115107913669065;1.31613634938866e-005;0.635095303595991;calculate IV;0;0;1;2;0;1;2;1;2;1
Tanzania;1;0.583333333333333;0.00431654676258993;0.000377433422623538;0.255845942228336;calculate IV;0;0;0;0;0;0;0;0;0;0
Thailand;0.916666666666667;0.75;0.113669064748201;0.0186046838454247;0.778738455492238;calculate IV;0;0;0;0;1;0;0;0;1;0
Togo;1;0.25;0.018705035971223;5.65043946349173e-005;0.487325604244449;calculate IV;0;0;0;0;0;0;0;0;0;0
Tokelau;1;1;0.00863309352517986;0.332907178295575;;calculate IV;0;0;1;2;1;1;2;0;1;0
Tonga;1;0.5;0.0273381294964029;0.252167741801627;0.730988406366673;calculate IV;0;0;0;0;1;1;2;0;1;0
Trinidad and Tobago;1;0.666666666666667;0.179856115107914;0.0246389686457361;0.670858714875221;calculate IV;0;0;0;1;1;0;0;0;1;0
Tunisia;1;0.25;0.103597122302158;6.40315994355559e-005;0.835134604047947;calculate IV;0;0;0;0;0;0;0;0;1;0
Turkey;0.833333333333333;0.666666666666667;0.107913669064748;0.0163637624680335;0.78600903910395;calculate IV;0;0;1;2;0;1;2;1;2;0
Turkmenistan;1;0;0.0820143884892086;0.000168924593695902;0.573983100805659;calculate IV;0;0;0;0;0;0;0;1;2;0
Turks and Caicos Islands;1;1;0.159712230215827;0.107589839560602;0.827471015916683;calculate IV;0;0;1;2;1;1;2;0;1;0
Tuvalu;1;1;0.0100719424460432;;0.701513067400275;calculate IV;0;0;1;2;1;1;2;0;0;0
Uganda;0.916666666666667;0.416666666666667;0.018705035971223;0.00013538814400813;0.393987030850855;calculate IV;0;0;0;0;0;0;0;0;0;0
Ukraine;1;0.583333333333333;0.0920863309352518;0.00548428122405604;0.734132442523089;calculate IV;0;0;1;2;1;1;2;1;2;1
United Arab Emirates;1;0.166666666666667;0.41294964028777;0.06962953750301;0.841422676360778;calculate IV;0;0;0;0;0;0;0;0;1;0
United Kingdom;0.666666666666667;1;0.438848920863309;0.118303500178793;0.902338376891334;calculate IV;2;1;1;2;1;1;2;1;2;2
United States;0;1;0.598561151079137;1;0.888779720966791;calculate IV;2;1;1;2;1;1;2;1;2;2
Uruguay;1;1;0.224460431654676;0.0503427032711818;0.858911377480841;calculate IV;1;1;0;0;1;1;2;0;1;0
Uzbekistan;1;0.0833333333333334;0.023021582733813;0.000398995094107751;0.628021222244056;calculate IV;0;0;1;2;0;0;0;1;2;1
Vanuatu;1;0.833333333333333;0.0359712230215827;0.00356594581904525;0.59402633130281;calculate IV;0;0;0;0;1;0;0;0;0;0
Venezuela;1;0.583333333333333;0.0877697841726619;0.00344004118463246;0.823737472980939;calculate IV;0;0;0;1;1;0;1;0;1;0
Vietnam;1;0.0833333333333334;0.037410071942446;6.54319022675747e-005;0.7512281391236;calculate IV;0;0;0;0;1;0;0;0;1;1
Yemen;0.916666666666667;0.333333333333333;0.00575539568345324;1.18324666396077e-005;0.579681666339163;calculate IV;0;0;0;0;0;0;0;0;0;0
Zambia;1;0.5;0.00719424460431655;0.000370846803438677;0.145608174494007;calculate IV;0;0;0;0;0;0;0;0;1;0
Zimbabwe;0.916666666666667;0.0833333333333334;0.0244604316546763;0.000822639587572765;0.131067007270584;calculate IV;0;0;0;0;0;0;1;0;1;0


Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap, a tdaxp Press Release

May 22 (BEIJING) – Dan of tdaxp, a Redefining the Gap. Redefining the Gap is an innovative contribution to grand strategic analysis, combining the superb vision of New York Times best-selling author Dr. Thomas P.M. Barnett with the power of hard numbers. Redefining the Gap, originally written for a graduate course in political science, was expanded and re-edited for the Internet. Redefining the Gap sheds new light Barnettian concepts, such as the “Functioning Core” and “Non-Integrating Gap,” as well as ideas such as Thomas Hobbes’ description of natural life as “nasty, brutish, and short.”

“Basically,” Dan remarked, “I took the description of ‘the Gap’ from The Pentagon’s New Map, and applied simple statistical methods to see whether it worked or not.” However, the analysis did not end there. “I was not content to see whether or not the model was good, but I wanted to see if it was actually better than existing models. So I used alternate and rival definitions of the Gap, from sources such as the United Nations, the Central Intelligence Agency, and others.” Surprisingly, the main finding of Redefining the Gap was a politically sensitive criticism of Barnett’s model. “If you merely define The Gap as nations that are either African or Islamic, the numbers say life in those nations is nastier, more brutish, and shorter than Barnett’s broader definition.” As Dan wrote in his prologue, “We are at war with Africa and Islam … We are at war for Africa and Islam.”

Redefining the Gap has already attracted interest throughout the blogosphere. “Of course, I was humbled when Dr. Barnett took interest in the project, even before completion,” remarked Dan. “Yet equally humbling were comments and contributions by readers and fellow bloggers. I am delighted and honored by the response Redefining the Gap has received from the blogosphere.”

On his accomplishment, Dan was philosophical. “I finished publishing Redefining the Gap the same day I visited a physically and spiritually abused cathedral, confiscated by the Communist Party decades ago. Every day during my visit to the capital of China, I am remanding what a disaster ‘shrinking the Core’ can be. A series of bad decisions led to the collapse of what Tom calls “Globalization I,” and the worst genocides and outrages of human history. A firm knowledge of what the Gap, and the Core, really are can help prevent a repeat.”

Redefining the Gap was published in 14 parts. It includes its original introduction and conclusion, as well as new prologue and results sections that are exclusive to electronic media. Redefining the Gap‘s literature review section covers geopolitics, early geopolitical theories, the Global South hypothesis, critical geopolitics, and Dr. Barnett’s PNM Theory. The report also includes a research design as well as a section on methods and operationalizations. The series concludes with an extensive bibliography, the computer logic used in the research, and the resulting scaled data.

Redefining the Gap is part of tdaxp‘s SummerBlog ’06, a series of series that will continue as Dan reports from the People’s Republic of China. Future installments include an overview of constructivist teaching methods including an interview with noted historical and educator Mark Safranski, an exploration of variations of USAF Colonel John Boyd’s “OODA” Loop, and an analysis of the popular web-log Creative Anarchy from the perspective of Creativity, Talent, and Expertise.

The tdaxp blog is available online at http://www.tdaxp.com.

Redefining the Gap 13, Appendix: Computer Code

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

Below is the perl code I used for data smoothing.

use strict;

 my %countries  = {}; my %countries_xml = {};

 run();

 # the grandparent function sub run {  getDVs();  getIVs();  getReports(); }

 # the three parent functions sub getDVs {  getIOs("Nonaligned Movement","Group of 15","Organization of the Islamic Conference","African Union","Group of 77","Group of 24");  getIGs("least developed countries","less developed countries");  getBarnettWorlds(); # barnett and worlds data in one file }

 sub getIVs {  getPoors();  #// "life in the Gap is poor"  getNasties();  #// "life in the Gap is nasty"  getShorts();  #// "life in the Gap is short"  getBrutals();  #// "life in the Gap is brutal"  getSolitaries(); #// "life in the Gap is solitary" }

 sub getReports {  getCountriesXML();  setIV();   setDVs();  getCSVView();  getXMLView(); }

 # the child functions

 # first, the children of getDVs() # specifically, getIOs(), getIGs(), getBarnettWorlds()

 sub getIOs { # get international organizations  my @ios = @_;

  my $io = "";

  my $file = "C:/Downloads/factbook/appendix/appendix-b.html";  my $line = "";

  my @fields = ();  my @nations = ();  my $nation = "";

  open(IOFILE,$file) || die "Couldn't open $file: $!";

  while ($line = ) {   if ($line =~ m/
/) {    foreach $io (@ios) {     if ($line =~ m/$io/ && $line =~ m//) {

      until ($line =~ m/>members/i) {       $line = ;      }      $line =~ s/(/,/g;      $line =~ s/)/,/g;      $line =~ s/

//g;

      @fields = split(/- ,/,$line);      @nations = split(/,/,$fields[$#fields]);      shift(@nations); # first one is junk

      foreach $nation (@nations) {       $nation = trim($nation);       $countries{$io}{$nation} = 1;      }     }         }   }  } }

 sub getIGs { # get international groups  my @igs = @_;

  my $ig = "";

  my $file = "C:/Downloads/factbook/appendix/appendix-b.html";  my $line = "";

  my @fields = ();

  my @nations = ();  my $nation = "";

  open(IOFILE,$file) || die "Couldn't open $file: $!";

  while ($line = ) {   if ($line =~ m/
/) {    foreach $ig (@igs) {     if ($line =~ m/$ig/ && $line =~ m//) {

      until ($line =~ m/are: /i) {       $line = ;      }      $line =~ s/(/,/g;      $line =~ s/)/,/g;      $line =~ s/

//g;

      @fields = split(/are: /, $line);

      $fields[$#fields] =~ s/;.*//g;

      @nations = split(/,/,$fields[$#fields]);

      foreach $nation (@nations) {       $nation = trim($nation);       $countries{$ig}{$nation} = 1;      }

     }         }   }  }  }

 sub getBarnettWorlds {  my $file = "c:/downloads/coregapworlds.csv";

  my @lines = ();  my @fields = ();

  my $line = "";

  open(BARNETT,$file) || die "Couldn't open $file: $!";  @lines = ;  close(BARNETT);

  foreach $line (@lines) {   @fields = split(/t/, $line);   #fields0 name   #fields1 old core new core gap   #fields2: first world second world third world   #fields3 neither g22 g77

   $countries{"CG"}{$fields[0]}  = $fields[1];   $countries{"Worlds123"}{$fields[0]} = $fields[2];   $countries{"Group of 22"}{$fields[0]}  = $fields[3];  }  }

 # second, the children of getIVs() # specifically, getPoors, getNasties, getShorts, getBrutals, getSolitaries

 sub getPoors {  getCIAInfo("poor","C:/Downloads/factbook/rankorder/2004rank.txt",["$",","]); }

 sub getNasties {  my $file = "c:/downloads/FIWrank7305.csv";

  my @fields = ();

  my $line = "";  my $state = "";  my $pr  = 0;  my $cl  = 0;

  open(FREE,$file) || die "Couldn't open $file: $!";  while ($line = ) {   @fields = split(/t/, $line);

   $state = $fields[0];   $pr = $fields[$#fields-2];   $cl = $fields[$#fields-1];

   if ($pr =~ m/[0-9]/ && $cl =~ m/[0-9]/) {    $countries{"nasty"}{$state} = ($pr + $cl) / 2;   }  }  close(FREE);  }

 sub getBrutals {  my $file_war = "c:/downloads/icb2.csv";  my $file_code = "c:/downloads/fields.csv";

  my @codes = ();  my @fields = ();

  my $line = "";  my $name = "";  my $state = "";  my $war  = "";  my $year_start = 0;  my $year_end = 0;

  # get the country codes  open(CODES,$file_code) || die "Couldn't open $file_code: $!";  while ($line = ) {   chomp($line);   $line =~ s/"//g;   @fields = split(/t/,$line);   $fields[0] = trim($fields[0]);   $fields[1] = trim($fields[1]);   $countries{"codes"}{$fields[1]} = $fields[0];   $countries{"wars"}{$fields[1]} = 0; # baseline 0 if country is in db  }  close(CODES);

  # get the wars  open(WARS,$file_war) || die "Couldn't open $file_war : $!";  while ($line = ) {   @fields = split(/t/,$line);   # $fields[4] = Actor   # $fields[5] = Start Year   # $fields[8] .. $fields[13] = war name   # $fields[57] (?) = year term

   $state  = $fields[4];   $year_start = $fields[5];   $year_end = $fields[5];

   $name  = "$fields[8]$fields[9]$fields[10]$fields[11]$fields[12]$fields[13]";

   if ($year_end > 1992) {    if ($year_start < 1992) {     $year_start = 1992;    }    $countries{"wars"}{$state} = $countries{"wars"}{$state} + ($year_end - $year_start + 1)   }  }  close(WARS);

  # now do the math  foreach $war (sort keys %{$countries{"wars"}}) {   $countries{"brutal"}{$countries{"codes"}{$war}} = $countries{"wars"}{$war};   $countries{"wars"}{$countries{"codes"}{$war}} = $countries{"wars"}{$war};  }  delete $countries{"codes"};  #delete $countries{"wars"}; }

 sub getShorts {  getCIAInfo("short","C:/Downloads/factbook/rankorder/2102rank.txt",["$"]); # life expectency }

 sub getSolitaries {  getCIAInfo("hosts","C:/Downloads/factbook/rankorder/2184rank.txt",[","]); # internet hosts  getCIAInfo("population","C:/Downloads/factbook/rankorder/2119rank.txt",[","]); # population

  my $key = "";

  foreach $key (keys %{$countries{"hosts"}}) {   if (exists($countries{"population"}{$key}) && exists($countries{"population"}{$key})) {    $countries{"solitary"}{$key} = $countries{"hosts"}{$key} / $countries{"population"}{$key};   }  } }

 # third, the children of getReports() # specifically, getCountriesXML, setIV, setDVs, getCSVView, getXMLView sub getCountriesXML {  my $file = "c:/downloads/rename.csv";

  my @keys = sort keys %countries;  my @nations = ();  my @lines = ();  my @fields = ();

  my $key  = "";  my $nation = "";  my $line = "";

  # first, simply transform the data structure  foreach $key (@keys) {   #print "Working on key $keyn";   @nations = sort keys %{$countries{$key}};   foreach $nation (@nations) {    if ($nation) {     $countries_xml{$nation}{$key} = $countries{$key}{$nation};    }   }  }

  # then, fix an errors  open(FILE,$file) || die "Couldn't open $file: $!";  @lines = ;  close(FILE);

  foreach $line (@lines) {   chomp($line);   @fields = split(/t/,$line);   # fields0: old name   # fields1: correct name

   if ($countries_xml{$fields[0]}) {    @keys = keys %{$countries_xml{$fields[0]}};    foreach $key (@keys) {     $countries_xml{$fields[1]}{$key} = $countries_xml{$fields[0]}{$key};    }    delete $countries_xml{$fields[0]};   }  }

  # remove countries that shouldn't exist  foreach $nation (sort keys %countries_xml) {   unless (exists($countries_xml{$nation}{"CG"})) {    delete $countries_xml{$nation};   }  }

  # then, back-propagate the changes  %countries = undef;  @nations = sort keys %countries_xml;  foreach $nation (@nations) {   @keys = sort keys %{$countries_xml{$nation}};   foreach $key (@keys) {    $countries{$key}{$nation} = $countries_xml{$nation}{$key};   }  } }

 sub setDVs {  my @nations = sort keys %countries_xml;

  my $nation = "";

  foreach $nation (@nations) {   if (    $countries_xml{$nation}{"African Union"}    == 1 ||    $countries_xml{$nation}{"Organization of the Islamic Conference"} == 1   ) {    $countries_xml{$nation}{"DV_AfricanIslam"}    = 0;   } else{    $countries_xml{$nation}{"DV_AfricanIslam"}    = 1;   }

   # BarnettCalculation   if ($countries_xml{$nation}{"CG"}      == 1) {    $countries_xml{$nation}{"DV_OCNCG"}     = 0;    $countries_xml{$nation}{"DV_CG"}     = 0;   } elsif ($countries_xml{$nation}{"CG"}     == 2) {    $countries_xml{$nation}{"DV_OCNCG"}     = 1;    $countries_xml{$nation}{"DV_CG"}     = 1;   } elsif ($countries_xml{$nation}{"CG"}     == 3) {    $countries_xml{$nation}{"DV_OCNCG"}     = 2;    $countries_xml{$nation}{"DV_CG"}     = 1;   }

   # Group of 22 / Group of 77   if ( $countries_xml{$nation}{"Group of 77"}     == 1 &&    $countries_xml{$nation}{"Group of 22"}     == 2   )  { # both means G77:0 but G22:1    $countries_xml{$nation}{"DV_G77"}     = 0;    $countries_xml{$nation}{"DV_G2277"}     = 1;   } elsif ($countries_xml{$nation}{"Group of 77"}     == 1) { # just G00 is 0 for both    $countries_xml{$nation}{"DV_G2277"}     = 0;    $countries_xml{$nation}{"DV_G77"}     = 0;   } else {    $countries_xml{$nation}{"DV_G77"}     = 1;    $countries_xml{$nation}{"DV_G2277"}     = 2;   }

   ## developed countries   if ( $countries_xml{$nation}{"least developed countries"}   == 1 &&    $countries_xml{$nation}{"less developed countries"}   == 1   )  {    $countries_xml{$nation}{"DV_LDCs"}     = 0;    $countries_xml{$nation}{"DV_LDCsLLDCs"}     = 0;   } elsif ($countries_xml{$nation}{"less developed countries"}   == 1) {    $countries_xml{$nation}{"DV_LDCs"}     = 0;    $countries_xml{$nation}{"DV_LDCsLLDCs"}     = 1;   } else {    $countries_xml{$nation}{"DV_LDCs"}     = 1;    $countries_xml{$nation}{"DV_LDCsLLDCs"}     = 2;   }

   ## worlds 1 2 3   if ($countries_xml{$nation}{"Worlds123"}     == 1) {    $countries_xml{$nation}{"DV_WorldsFreeComNon"}    = 2;   } elsif ($countries_xml{$nation}{"Worlds123"}     == 2) {    $countries_xml{$nation}{"DV_WorldsFreeComNon"}    = 1;   } elsif ($countries_xml{$nation}{"Worlds123"}     == 3) {    $countries_xml{$nation}{"DV_WorldsFreeComNon"}    = 0;   } 

   # Group of 15 / NAM   if ( $countries_xml{$nation}{"Nonaligned Movement"}) {    $countries_xml{$nation}{"DV_Nalign"}     = 0;    if ($countries_xml{$nation}{"Group of 15"}) {      $countries_xml{$nation}{"DV_G15Nalign"}    = 1;    } else {     $countries_xml{$nation}{"DV_G15Nalign"}    = 0;    }   } else {    $countries_xml{$nation}{"DV_Nalign"}     = 1;    $countries_xml{$nation}{"DV_G15Nalign"}     = 2;   }  }    } 

 sub setIV {  my @nations = sort keys %countries_xml;  my $nation = "";

  my @keys = ();  my $key  = "";

  scaleDataXML("brutal");  scaleDataXML("nasty");  scaleDataXML("poor");  scaleDataXML("solitary");  scaleDataXML("short");   

  foreach $nation (@nations) {   $countries_xml{$nation}{"IV_brutal"} = 1 - $countries_xml{$nation}{"brutal"};   $countries_xml{$nation}{"IV_nasty"} = 1 - $countries_xml{$nation}{"nasty"};   $countries_xml{$nation}{"IV_poor"} = $countries_xml{$nation}{"poor"};   $countries_xml{$nation}{"IV_solitary"} = $countries_xml{$nation}{"solitary"};   $countries_xml{$nation}{"IV_short"} = $countries_xml{$nation}{"short"};  } }

 sub getCSVView {  my @nations = keys %countries_xml;  my $nation = "";

  @nations = sort @nations;

  open (CSVFILE,">report.csv") || die "Couldn't open report.csv: $!";  print CSVFILE  "Nation;Brutal;Nasty;Poor;Solitary;Short;IV;OCNCG;CG;G77;G2277;AfroIslam;Nalign;G15Nalign;LDCs;LDCsLLDCs;FreeComNonn";  print CSVFILE "BrutalnNastynPoornSolitarynShortnIVnOCNCGnCGnG77nG2277nAfroIslamnNalignnG15NalignnLDCsnLLDCsLDCsnFreeComNonnnn";

  foreach $nation (@nations) {   if (exists($countries_xml{$nation}{"DV_CG"})) {    print CSVFILE (     $nation      . ";" .     $countries_xml{$nation}{"IV_brutal"}   . ";" .      $countries_xml{$nation}{"IV_nasty"}   . ";" .      $countries_xml{$nation}{"IV_poor"}   . ";" .      $countries_xml{$nation}{"IV_solitary"}   . ";" .      $countries_xml{$nation}{"IV_short"}   . ";" .      "calculate IV"      . ";" .          $countries_xml{$nation}{"DV_OCNCG"}   . ";" .     $countries_xml{$nation}{"DV_CG"}   . ";" .      $countries_xml{$nation}{"DV_G77"}   . ";"  .     $countries_xml{$nation}{"DV_G2277"}   . ";"  .     $countries_xml{$nation}{"DV_AfricanIslam"}  . ";" .     $countries_xml{$nation}{"DV_Nalign"}   . ";" .     $countries_xml{$nation}{"DV_G15Nalign"}   . ";" .     $countries_xml{$nation}{"DV_LDCs"}   . ";"  .     $countries_xml{$nation}{"DV_LDCsLLDCs"}  . ";"  .     $countries_xml{$nation}{"DV_WorldsFreeComNon"}  . "n"    );    }  }  close(CSVFILE); }

 sub getXMLView {  my @keys = keys %countries;  my @nations = ();  my @values = ();

  my %names = {};

  my $key  = "";  my $nation = "";  my $value = "";

  open (XMLFILE,">report.xml") || die "Couldn't open report.xml: $!";  print XMLFILE "n";  foreach $nation (sort keys %countries_xml) {   print XMLFILE "tn";   foreach $value (sort keys %{$countries_xml{$nation}}) {    print XMLFILE "tt$countries_xml{$nation}{$value}n";   }   print XMLFILE "tn";  }  print XMLFILE "n";  close(XMLFILE);

 }

 # fourth, the grandchildren go here # specifically, getCIAInfo, scaleDataXML, scaleData, and trim sub getCIAInfo {  my $record = shift(@_);  my $file = shift(@_);  my @to_remove = @{shift(@_)};

  my $remove = "";

  my $line = "";  my @lines = ();  my $linec = 0;

  my $field = "";  my @fields = ();

  open(FILE, $file) || die "Couldn't open $file: $!";  @lines = ;  for ($linec=2;$linec<=$#lines;$linec++) {    @fields = split(/t/, $lines[$linec]);

   if ($fields[1]) { # if the country is named

    # rank order is $fields[0]    # country is $fields[1]    # GDP per capiat is $fields[2]    # year est is $fields[3]

    $fields[1] = trim($fields[1]);

    foreach $remove (@to_remove) {     $fields[2] =~ s/[$remove]//g;    }    #print "Length of temp is $#temp and temp0 is $temp[0]n";    #$fields[2] = join("",@temp);    $fields[2] = trim($fields[2]);

    $countries{$record}{$fields[1]} = $fields[2];;   }  } }

 sub scaleDataXML {  print "Entering scaleDataXMLn";  my $record = shift(@_);

  my @nations = sort keys %countries_xml;  my $nation = "";

  my $min  = $countries_xml{$nations[0]}{$record};  my $max  = $countries_xml{$nations[0]}{$record};

  # first, find min and max  foreach $nation (@nations) {   if (exists($countries_xml{$nation}{$record})) {    if ($max < $countries_xml{$nation}{$record}) {     $max = $countries_xml{$nation}{$record};    }    if ($min > $countries_xml{$nation}{$record}) {     $min = $countries_xml{$nation}{$record};    }   }  }

  print "$record goes from $min to $maxn";

  # second, scale  foreach $nation (@nations) {   if (exists($countries_xml{$nation}{$record})) {    $countries_xml{$nation}{$record} = ($countries_xml{$nation}{$record} - $min) / ($max - $min);   }  } }

 # function from http://www.somacon.com/p114.php sub trim($) {  my $string = shift;  $string =~ s/^s+//;  $string =~ s/s+$//;  return $string; } 

Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap 12, Bibliography

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

Below is the bibliography for this project. Many of the documents cited can be obtained from JSTOR.


Agnew, John A. 1995. Mastering Space. New York: Routledge.

Ansah, Esi E. 2002. Theorizing the Brain Drain. African Issues 30: 21-24.

Ansley, Fran. 2001. Inclusive Boundaries and Other (Im)possible Paths toward Community Development in a Global World. University of Pennsylvania Law Review 150: 353-417.

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Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap 11, Results

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md
Nation Brutal Nasty Poor Sol. Short IV
OCNCG -0.16 0.47 0.73 0.64 0.43 0.65
CG -0.14 0.46 0.68 0.56 0.41 0.61
G77 -0.04 0.52 0.5 0.45 0.51 0.65
G2277 -0.09 0.48 0.47 0.42 0.51 0.61
AfroIslam 0.05 0.6 0.34 0.31 0.63 0.67
Nalign – 2 0.01 0.58 0.43 0.4 0.55 0.67
Nalign – 3 0.001 0.57 0.41 0.38 0.56 0.66
LDC – 2 -0.08 0.31 0.55 0.44 0.41 0.49
LDC – 3 -0.08 0.38 0.54 0.4 0.62 0.59
Worlds -0.14 0.42 0.7 0.59 0.48 0.64


The tables you see above are the coefficients of correlations for the models described in this series to the measures Barnett describes. This study looked at the population of all states, not a sample of states, so the margin of error is +/-0%. These numbers are completely internally valid — they describe carefully derived measures. The difference between them is significant. However, the greater question of whether or not the correct measures were used is a different subject.

Chirol from Coming Anarchy suggested that I look at the Four Flows instead of brutality, nastiness, etc. It may be that I misconstrued what Barnett meant in the passages of Pentagon’s New Map where he gives the definitions.

Regardless of the meaning of these numbers, a short discussion of the results is included below.

Brutality. This was the biggest surprise. For most measures, including Barnett’s Core-Gap and Old Core – New Core – Gap, brutality decreases in the Core. This is because the University of Maryland’s ICBP database that I used measures the countries involved in wars. Besides ignoring some sub-state conflicts, the project would this could the Kosovo War as mostly a “Core” war. After all, nearly all the combatants — America, England, etc, – are Core states.

Still, the Afroislamic Gap is the best predictor of brutality. Afromuslim countries go to war more often than any other states. The worst predictor was the Old Core – New Core – Gap model.

Nastiness. Measured through lack of political freedoms and human rights, Afromuslim states fail again. The worst measure is merely defining Lesser Developed Countries (LDCs) as the Gap.

Poverty. Here, Barnett’s economic determinist model shines through. The very best measure is Old Core – New Core – Gap, and the second best is a more general Core – Gap. Interestingly, here the Afroislam model scored the worst — a reversal of our experience with Brutality — though here at least, both show a positive correlation between being in a “Gap” and general badness.

Solitude. I modified Barnett’s measure, from internet hosts in a country to internet hosts per capita. It would make little sense to call a very populous state the most connected state if only a small fraction of its population had access to the internet. The results here are the similar to the ones for poverty — Old Core – New Core – Gap the best gauge, Afroislam the worst. Interestingly, here a 1st, 2nd, and 3rd world model of the globe does better than Barnett’s simpler Core-Gap model.

Shortness. Want to die early? Move to an African or Islamic country. Only looking at the world from t he point of view of Developed — Lesser Developed — Least Developed states comes close to this. The very worst predictor is Barnett’s Core – Gap model, though Barnett’s Old Core – New Core – Gap model is only slightly better.

All in All. Averaging these scores together, the AfroIslam model remains the best for describing the Hobbesian states we fight against and for. All in all, however, the ups in one Hobbesian measure seam to compensate for the downs in others, making all of these pretty good. Still, this shows a danger of just looking at an agregate measure instead of more specific measures.

A Note on the Result. I’m not a statistician. I have advanced training in predicate calculus and relational algebra, but the pseudo-math of statistics is not my forte. I would much rather have my analysis short to pieces than for it to just sit here. Likewise, I used an extremely simple tool to run these numbers.

Please, correct me. Show me where I am wrong. And then, let’s shrink the Gap — Afroislamic or not.


Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap 10, Limitations and Conclusion

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

Halford Mackinder said that “every century has its own geographical perspective,” and it may even be true that “every century has its own geographical stereotype” (Meinig 1956:553). Geopolitical analysis is necessarily limited to some conception of the world. This research design seeks to test a geopolitical view of the present world. It is not a test throughout time. It makes no claim to be. That makes this study no less valuable.


The effects of this study depend on the truth or falsity of the hypothesis. In each case, the most interesting results would be if the hypothesis is false.

A failure of the first hypothesis — that is, negative or no correlations for the binary Core-Gap value — is very unlikely. It is doubtful that life in in the “Core” is more brutal, nastier, shorter, poorer, and more solitary than life in the Gap. However, given the broad definition of “Core” here, negative or no correlation for at least some of the variables is possible. This raises a more delicate point: if just one of the categories has a negative correlation with the Core-Gap variable, there will be a temptation to simply say it was poorly defined. Regardless of the ultimate conclusion, though, such a result would pave the way to future research.

In general, the same conclusions will hold true if hypothesis two is demonstrated false. However, a negative results here would be somewhat less surprising. If in general “new core” states are more livable than old core states, which seems somewhat reasonable (is “Old Core” Spain truly better than “New Core” South Korea?), this would skew the results.

Even if hypothesis one and two hold true, however, the utility of Barnett’s “new map” will be undermined if hypothesis three or four are shown to be negative. If for instance a geopolitical categorization based on the G77 or the Nonaligned movement are more accurate that Barnett’s concept, then PNM’s goal as a grand strategy for the United States is unlikely to be fulfilled. After all, why go with something new and strange when something old and familiar does the job better? Likewise, if defining the Gap simply as the Organization of the Islamic Conferences and the African Union gives better values than Barnett’s current summary, This is not just an academic concern, but may in turn effect base closings and even how and when to go to war.

If disproving the third and fourth hypotheses would be the most interesting, disproving the fifth would be the most boring. The New Core – Old Core divide naturally seems somewhat artificial, leaving Australia and New Zealand in the “new” world while confining Spain and Greece to the “Old.”

On the flip side, finding all five of the hypotheses true would help validate Barnett’s claims. More work would have to be done. After all, a study that uses the data suggested by the theorist might be suspect, but it would be a good first step.


Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap 9, Methods and Operationalizations

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

Poverty will be measured by GDP per capita, measured by purchasing power parity (CIA 2006c). Estimates are recent, with most being from 2004 or 2005. The information is listed in US Dollars. My study will scale GDP per capita so that poorest value is 0 and the richest value is 1. For each state, it’s value will be calculated by taking the difference between that state’s value and the lowest state’s value, divided by the difference between the highest state’s value and the lowest state’s value. The logic to read in and scale this data is included in the appendix, particularly in the function scaleData().


Nastiness will be measured by a state’s Freedom in the World measure (Freedom House 2006). Freedom House uses two 7-point scales for political freedoms and civil rights. The most repressive, and thus “nastiest,” regimes would score a 7 on both counts, while the least nasty would score 1. This study will take the mean of the two values and scale them, with the most free state having a score of “1” and the least free state having a score of “0.” The logic to read in and scale this data is included in the appendix.

Shortness will be measured by life expectancy (CIA 2006d). Estimates are recent, with all dating from 2006. The information is listed in years. The study will scale life expectancy so that shortest value is “0” and the longest value is “1.” The logic to read in and scale this data is included in the appendix.

Brutality will be calculated from the International Crisis Behavior project (CIDCM 2006). Wars which have been fought at least in part after 1992 will be considered. Wars are considered dyadic. Brutality will be measured as the sum of wars per year. For example, a state that is involved in two wars each against two states that each last two years would have a brutality score of “8.” The study will then scale the scores, with the least brutal state having a score of 0 and the most “brutal” state having a score of 1. The logic to read and scale this data is included in the appendix.

Solitariness will be measured by the number of Internet hosts in a country per capita. This will be derived from two different measures: the number of Internet hosts per country divided by each country’s population (CIA 2006b; CIA 2006e). The population of Internet hosts and people are both estimated down to individual hosts and persons. All estimates of Internet hosts date form 2005 while all estimates of population date are for July 2006. The result will then be scaled, with the state with the highest number of Internet hosts per capita as “1,” and the state with the lowest number as “0.” The logic to read and scale this data is included in the appendix.

The model will contain eight dependent variables, with two of them relating directly to Barnett’s “new map.” All will be ordinal values, with the lowest values referring to the Gap (or its supposed equivalent), and the highest values referring to the Functioning Core (or its supposed equivalent). Three of the variables will have two possible variables, while the other five will have three.

The first dependent variables look at are Barnett’s models. Barnett has described his cartography in two different ways: as comprising a “Functioning Core” and a “Non-Integrating Gap,” as well as of comprising an “Old Core,” a “New Core,” and the “Gap.” The difference is that the more detailed model separates peripheral or newly developed economies — Argentina, South Africa, South Korea, etc. – from the Cold War pillars of North America, Western Europe, and Japan. The binary variable will rate the Gap as 0 and the Core as 1. The ternary variable will rate the Gap as 0, the New Core as 1, and the Old Core as 2.

As Barnett’s PNM model is essentially a critical North-South view of the world, most of the other dependent variables for rival hypotheses will be taken from other concepts that are analogous to the Global South – the Non-Aligned Movement and the Third World (Holm 1990:2). Additionally, one more will be added to address a cultural and race based criticism of Barnett’s map.

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The Nonaligned Movement

The next four variables relate to an International Organizational definition of the global south. It relies on two NGOs, the G77 and the G24. The G77 is an organization of undeveloped countries, and the G24 is its executive steering committee. G77 nations are assumed to be similar to Barnett’s “Gap,” while G24 to his “New Core.” Therefore, the binary variable for this shall map the G77 to 0 and the rest of the world to 1. The ternary variable will rate nations only in the G77 as 0, states in the G77 and G24 as 1, and all other states as 2. Dependent variables for the Non-Aligned Movement and its executive steering committee, the G-15, will be calculated in the same fashion. The G77 and the Nonaligned Movement are of about the same age, though the G77 traditionally has a broader membership (Geldart and Lyon 1980-1981:80), so it makes sense to examine both of these alternatives.

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The Group of Seventy-Seven

An “international group” perspective will be used to divide countries into Least Developed Nations, Less Developed Nations, and the rest of the world (CIAa 2006a). The measures of Least Developed Countries (LLDCs) and Less Developed Countries (LDCs) originate at the United Nations. The distinction is meant to separate countries which have a reasonable chance of developing with those facing severe structural maladies (Horowitz 1985-1986:47).The same ternary and binary divisions will be used for these are as predicted. When viewed binarily, LLDc and LDCs will both be valued at 0, with other states valued at 1. Viewed as ternary, LLDCs will have the value of 0, LDCs of 1, and all other states of 2.

The term Global South originated in part as a reaction against the fading “Third World” model that was born in the 1950s. This model will use this model, taking as its definition of “worlds” from a map. Formerly and currently Communist states, from Poland to Vietnam, are in the Second World and labeled “2.” The United States and other “free” states are in the First World and labeled 1. The rest of the world, which closely matches traditional views of the Global South, is measured at 3.
One more possible dependent variable, this one binary, will calculated. This addresses the concern that the “new map” is essentially just an encirclement of Africa and majority Muslim states, with the rest of the “Gap” (the Caribbean, South-East Asia, etc) as more-or-less superfluous. An earlier version of Barnett’s work made this explicit, “with only Central Asia, the Middle East, and Africa trapped on the outside, noses pressed to the glass” (Barnett 2004:109). This variable will label as “0” any state in either the Organization of Islamic States or the African Union, and label all other states as 1. The often culturally destructive actions of newly independent African states (Beckstrom 1974:698) and their stagnating economies (Hentz 1997:32), as well as increasing instability through much of the Arab (Sayigh 1991:487) and Muslim (Menon 1995:154) world, argue that this alternative is a reasonable one.

The following specific predictions are made:

1.The Core-Gap binary variable will have have a positive correlation to each of the individual variables.
2.The Old-Core-New-Core-Gap ternary variable will have a positive correlation to each of the independent variables.
3.The Core-Gap binary variable will have a higher correlation to each of the independent variables than any of the other binary dependent variables.
4.The Old-Core-New-Core-Gap ternary variable will have a higher correlation to each of the independent variables than any of the other ternary dependent variables.
5.The Old Core-New Core-Gap ternary variable will have a higher correlation to each of the independent variables than the Core-Gap binary variable.


Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes

Redefining the Gap 8, The Research Design

Note: This is a selection from Redefining the Gap, part of tdaxp‘s SummerBlog ’06

tdaxps_new_map_md

Yet in spite of the potential consequences of Barnett’s work, little has been done to test it. For instance, do the measures he gives for the “Gap” actually correlate with being in the Gap? Does another accepted model work better?


This model predicts that Barnett’s more granular summary, divided into the Old Core, New Core, and Gap, is both positive for each of the measures he defines as well as superior to alternate ternary models of the Global South. Likewise, this model predicts that his simpler version, with a united Core and the Gap, is both positive for each of those measures as well as superior to alternate binary measures. Last, this paper predicts that the more granular version is superior on these same counts to the less granular one.

This model will contain five independent variables, and a sixth which is a composite of the five. The five independent variables are the measures of poverty, nastiness, shortness, brutality, and solitariness previously described. All independent variables will come form Barnett’s first measurement of the Gap.

All data for this study will come from the CIA’s World Factbook, Freedom House’s Freedom in the World study, or the University of Maryland’s International Crisis Behavior Project. The World Factbook has been used in academic studies down the decades (Evans 2003:1311; Lennox 1993:705; Partem 1983:8). Freedom House is a leader in measuring democratic rights in countries, and is a standard on which other measures are judged (Davenport and Armstrong 2004 541; Vanhanen 2000 251). The University of Maryland’s database is also a leading statistical resource, but of war instead of rights (Caprioli and Boyer 2001 504; Oneal and Bryan 1995 380).


Redefining the Gap, a tdaxp series:
Redefining the Gap 1. Prologue
Redefining the Gap 2. Summary
Redefining the Gap 3. Introduction to Geopolitics
Redefining the Gap 4. First Geopolitical Theories
Redefining the Gap 5. The North and the South
Redefining the Gap 6. Critical Geopolitics
Redefining the Gap 7. The Pentagon’s New Map
Redefining the Gap 8. The Research Design
Redefining the Gap 9. Methods and Operationalizations
Redefining the Gap 10. Limitations and Conclusion
Redefining the Gap 11. Results
Redefining the Gap 12. Bibliography
Redefining the Gap 13. Appendix: Computer Code
Redefining the Gap 14. Appendix: National Codes