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Record W2610237182 · doi:10.1037/apl0000178

The dynamics of punishment and trust.

2017· article· en· W2610237182 on OpenAlexaff
Long Wang, J. Keith Murnighan

Bibliographic record

VenueJournal of Applied Psychology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPunishment (psychology)PsycINFOPsychologyDilemmaSocial psychologyInterpersonal communicationSocial dilemmaInterpersonal relationshipEconomic JusticeLawPolitical scienceMEDLINE

Abstract

fetched live from OpenAlex

The trade-off between mercy and justice is a classic moral dilemma, particularly for organizational leaders and managers. In 3 complementary studies, we investigated how resolving the "punishment dilemma" influences interpersonal trust. Study 1 used controlled scenarios to show that uninvolved observers trusted leaders who administered large or medium punishment more than leaders who administered no punishment when transgressors deserved punishment. At the same time, large punishment decreased trust more than medium or no punishment for less deserving targets. Study 2's similar scenarios showed that leaders who administered punishment lost trust when they subsequently received benefits even though it was not clear whether their benefits resulted from their act of punishment. Study 3 provided a behavioral replication of these results. These findings suggest that people trusted punishers more than nonpunishers, but only when punishers' motives were not personal revenge. In the discussion, we explore the practical and theoretical implications of these results for organizations. (PsycINFO Database Record

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 score0.471
Threshold uncertainty score0.243

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.331
Teacher spread0.259 · 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

Citations55
Published2017
Admission routes1
Has abstractyes

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