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Record W4414846063 · doi:10.1108/ijm-07-2024-0489

Psychological climate for diversity, racial/ethnic workplace discrimination, and prosocial voice behavior: the moderating role of social dominance orientation

2025· article· en· W4414846063 on OpenAlexaffabout
Pegah Sajadi, Christian Vandenberghe

Bibliographic record

VenueInternational Journal of Manpower · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsProsocial behaviorSocial dominance orientationDiversity (politics)Structural equation modelingDominance (genetics)Positive relationshipEmployee voice

Abstract

fetched live from OpenAlex

Purpose This study aims at expanding research on the antecedents, consequences, and boundary conditions associated with racial/ethnic discrimination in workplaces. Design/methodology/approach We collected longitudinal data from 826 employees affiliated with eight Canadian governmental organizations. Structural equation modeling was used to test the hypotheses through Mplus 7.4. Findings Social dominance orientation (SDO) moderated the relationship between psychological climate for diversity and perceived racial/ethnic workplace discrimination (REWD): the relationship was negative among low-SDO employees and turned positive among high-SDO employees. Additionally, perceived REWD was positively related to prosocial voice behavior among low-SDO employees but negatively among high-SDO employees. Furthermore, the indirect effect of climate for diversity on prosocial voice was positive for high-SDO employees but negative for low-SDO employees. Originality/value There has been little research examining the relationship between psychological climate for diversity and REWD. We show that a positive diversity climate lowers perceived REWD, with employee SDO as a key moderator. Additionally, we respond to calls to explore the behavioral implications of workplace discrimination and examine the relationship between perceived REWD and employee voice. We show that the nature of this relationship depends on employees’ SDO level.

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)0.001
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 score0.728
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.417
Teacher spread0.383 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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