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Record W2149950375 · doi:10.1177/0018726713485609

Organizational justice: New insights from behavioural ethics

2013· article· en· W2149950375 on OpenAlexaff
Jonathan R. Crawshaw, Russell Cropanzano, Chris Bell, Thierry Nadisic

Bibliographic record

VenueHuman Relations · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsYork University
Fundersnot available
KeywordsOrganizational justiceEconomic JusticeInjusticeSociologyContext (archaeology)Work (physics)Interactional justicePerceptionEmpirical researchAntecedent (behavioral psychology)PsychologySocial psychologyEngineering ethicsPublic relationsOrganizational commitmentPolitical scienceEpistemologyLaw

Abstract

fetched live from OpenAlex

Both organizational justice and behavioural ethics are concerned with questions of ‘right and wrong’ in the context of work organizations. Until recently they have developed largely independently of each other, choosing to focus on subtly different concerns, constructs and research questions. The last few years have, however, witnessed a significant growth in theoretical and empirical research integrating these closely related academic specialities. We review the organizational justice literature, illustrating the impact of behavioural ethics research on important fairness questions. We argue that organizational justice research is focused on four reoccurring issues: (i) why justice at work matters to individuals; (ii) how justice judgements are formed; (iii) the consequences of injustice; and (iv) the factors antecedent to justice perceptions. Current and future justice research has begun and will continue borrowing from the behavioural ethics literature in answering these questions.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.040
Scholarly communication0.0070.011
Open science0.0020.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.400
GPT teacher head0.446
Teacher spread0.046 · 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 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

Citations120
Published2013
Admission routes1
Has abstractyes

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