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Record W3024490851 · doi:10.3389/fpsyg.2020.00812

On the Value of Considering Specific Facets of Interactional Justice Perceptions

2020· article· en· W3024490851 on OpenAlexaff
Evelyne Fouquereau, Alexandre J. S. Morin, Tiphaine Huyghebaert‐Zouaghi, Séverine Chevalier, Hélène Coillot, Nicolas Gillet

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsConcordia University
Fundersnot available
KeywordsPsychologyInteractional justiceInterpersonal communicationSocial psychologyPerceptionEconomic JusticeValue (mathematics)Construct (python library)Procedural justicePolitical science

Abstract

fetched live from OpenAlex

This research seeks to verify the value of considering specific perceptions of interpersonal and informational justice over and above employees’ global perceptions of interactional justice. In Study 1, we examined the underlying structure of employees’ perceptions of interactional justice at work by contrasting first-order and bifactor representations of their ratings. The results showed that employees’ perceptions of interactional justice simultaneously reflected a global overarching interactional justice construct, together with two specific dimensions (interpersonal and informational justice). Study 2 examined latent profiles of employees based on their global (interactional justice) and specific (interpersonal and informational justice) levels of interactional justice. The results revealed five distinct interactional justice profiles related to employees’ levels of anxiety and emotional exhaustion.

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)0.014
metaresearch head score (Gemma)0.049
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.002
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 head0.035
GPT teacher head0.288
Teacher spread0.253 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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