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Record W2149950375 · doi:10.1177/0018726713485609

Organizational justice: New insights from behavioural ethics

2013· article· en· W2149950375 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.007

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