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Record W2557460076 · doi:10.1177/0022002716680266

Self-censorship of Conflict-related Information in the Context of Intractable Conflict

2016· article· en· W2557460076 on OpenAlexfundno aff
Eldad Shahar, Boaz Hameiri, Daniel Bar‐Tal, Amiram Raviv

Bibliographic record

VenueJournal of Conflict Resolution · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science Foundation
KeywordsCensorshipContext (archaeology)Social psychologyTransparency (behavior)Conflict resolutionPerceptionPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Self-censorship is of great importance in societies involved in intractable conflict. In this context, it blocks information that may contradict the dominant conflict-supporting narratives. Thus, self-censorship often serves as an effective societal mechanism that prevents free flow and transparency of information regarding the conflict and therefore can be seen as a barrier for a peacemaking process. In an attempt to understand the potential effect of different factors on participants’ willingness to self-censor (WSC) conflict-related information, we conducted three experimental studies in the context of the Israeli–Palestinian conflict. Study 1 revealed that perception of distance from potential information recipients and their disseminating capabilities lead to higher WSC. Study 2 replicated these results and also showed that fulfilling different social roles has an effect on the WSC. Finally, study 3 revealed that the type of information has a major effect on WSC.

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)0.005
metaresearch head score (Gemma)0.031
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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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