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Record W2948308424 · doi:10.1080/03050629.2019.1622543

Evaluating the influence of international norms and shaming on state respect for rights: an audit experiment with foreign embassies

2019· article· en· W2948308424 on OpenAlexaboutno aff
Zhanna Terechshenko, Charles Crabtree, Kristine Eck, Christopher J. Fariss

Bibliographic record

VenueInternational Interactions · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
FundersVetenskapsrådet
KeywordsHuman rightsShameNorm (philosophy)Compliance (psychology)State (computer science)Political scienceForeign policyInternational conflictInternational communityAuditSocial psychologyPsychologyLawEconomicsAccounting

Abstract

fetched live from OpenAlex

How do international norms affect respect for human rights? We report the results of an audit experiment with foreign missions that investigates the extent to which state agents observe international norms and react to the potential of international shaming. Our experiment involved emailing 669 foreign diplomatic missions in the United States, Canada, and the United Kingdom with requests to contact domestic prisoners. According to the United Nations, prisoners have the right for individuals to contact them. We randomly varied (1) whether we reminded embassies about the existence of an international norm permitting prisoner contact and (2) whether the putative email sender is associated with a fictitious human rights organization and, thereby, has the capacity to shame missions through naming and shaming for violating this norm. We find strong evidence for the positive effect of international norms on state respect for human rights. Contra to our expectations, though, we find that the potential of international shaming does not increase the probability of state compliance. The positive effect of the norms cue disappears when it is coupled with the shaming cue, suggesting that shaming might have a 'backfire' effect.

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)0.000
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 score0.877
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.442
Teacher spread0.379 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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