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Record W2745994562

On the Psychology of Perceived Procedural Justice : Experimental Evidence that Behavioral Inhibition Strengthens Reactions to Voice and No-Voice Procedures

2017· article· en· W2745994562 on OpenAlexaff
Liesbeth Hulst, Kees van den Bos, A.J. Akkermans, E. Allan Lind

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsProcedural justicePsychologyAntecedent (behavioral psychology)Economic JusticeSocial psychologyAction (physics)Affect (linguistics)Process (computing)Cognitive psychologyCommunicationPerceptionLawPolitical scienceNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

This paper argues that when people try to sort out whether they are treated in just or unjust manners, they will tend to inhibit ongoing action to pause and check what is going on. In this way, behavioral inhibition can facilitate the procedural justice judgment process of interpreting whether you were treated in just or unjust ways. We further note that receiving opportunities to voice opinions is a key antecedent of perceived procedural justice. Following this line of reasoning, we argued that an experimental manipulation that strengthens behavioral inhibition should lead people to respond more strongly to receiving voice versus being withheld voice in decision-making procedures. In two studies, we found that reminding people of times they acted with public inhibitions (versus not reminding them) indeed led to more negative procedural judgments following no-voice procedures (Study 1) and to more positive procedural justice judgments following voice procedures (Study 2). These findings suggest that higher levels of behavioral inhibition may lead people to become more sensitive to what happens in their environments and, hence, affect the justice judgment process.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.841

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.196
GPT teacher head0.377
Teacher spread0.181 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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