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Record W2098877340 · doi:10.2307/2586386

Political Parties and Foreign Aid

2000· article· en· W2098877340 on OpenAlexaff
Jean‐Philippe Thérien, Alain Noël

Bibliographic record

VenueAmerican Political Science Review · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLISRELPoliticsForeign policyPolitical scienceLeft-wing politicsWelfare stateRelevance (law)Foreign policy analysisPolitical economyState (computer science)WelfareDemocracyEconomicsStructural equation modelingLaw

Abstract

fetched live from OpenAlex

The influence of partisan politics on public policy is a much debated issue of political science. With respect to foreign policy, often considered as above parties, the question appears even more problematic. This comparison of foreign aid policies in 16 OECD countries develops a structural equation model and uses LISREL analysis to demonstrate that parties do matter, even in international affairs. Social-democratic parties have an effect on a country's level of development assistance. This effect, however, is neither immediate nor direct. First, it appears only in the long run. Second, the relationship between leftist partisan strength and foreign aid works through welfare state institutions and social spending. Our findings indicate how domestic politics shapes foreign conduct. We confirm the empirical relevance of cumulative partisan scores and show how the influence of parties is mediated by other political determinants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.364
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations261
Published2000
Admission routes1
Has abstractyes

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