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Record W2892602810 · doi:10.1080/03050629.2019.1522308

The psychological effects of state socialization: IGO membership loss and respect for human rights

2018· article· en· W2892602810 on OpenAlexaboutno aff
Gina L. Miller, Ryan M. Welch, Andrew Vonasch

Bibliographic record

VenueInternational Interactions · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
FundersUniversity of AlabamaSchool of Politics and Global Studies, Arizona State UniversityArizona State UniversityFlorida State University
KeywordsSocializationHuman rightsIncentiveQuarter (Canadian coin)State (computer science)Social psychologyPolitical scienceSociologyEconomicsPsychologyLawMicroeconomics

Abstract

fetched live from OpenAlex

We present an interdisciplinary theory that considers how loss of membership in international organizations affects states’ human rights practices. Drawing mostly from social psychology and international relations research, we argue that states are socialized into the international community through a process of social influence, whereby they are incentivized to comply with group norms by the promise (threat) of social rewards (punishments). Social influence occurs when states form social bonds through interactions with other states. When social bonds are severed, fewer opportunities for social influence occur due to lower information to both the remaining states and the state that lost those social bonds. Thus, we hypothesize that the loss of membership from IGOs reduces incentives to comply with group norms and adversely affects human rights practices at home. A combination of propensity score matching/regression and autoregressive distributed lag (ADL) models on a global cross-section across the years 1978–2012 supports the theory. Specifically, losing at least one IGO membership leads to a long-run drop in human rights respect of about one quarter to one half standard deviation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.460
Teacher spread0.407 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations10
Published2018
Admission routes1
Has abstractyes

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