MétaCan
Menu
Back to cohort
Record W2070642187 · doi:10.1037/a0012696

Looking inside and out: The impact of employee and community demographic composition on organizational diversity climate.

2008· article· en· W2070642187 on OpenAlexafffund
S. Douglas Pugh, Joerg Dietz, Arthur P. Brief, Jack W. Wiley

Bibliographic record

VenueJournal of Applied Psychology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDiversity (politics)WorkforceContext (archaeology)Organisation climatePublic relationsPerceptionComposition (language)Social psychologyPsychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

An organization's diversity climate refers to employees' shared perceptions of the policies and practices that communicate the extent to which fostering diversity and eliminating discrimination is a priority in the organization. The authors propose a salient element of the organizational context, the racial composition of the community where the organization is located, serves an important signaling function that shapes the formation of climate perceptions. In a study of 142 retail bank units in the United States, evidence is found for a relationship between the racial composition of an organization's workforce and diversity climate that is moderated by the racial composition of the community where the organization is located. The results suggest that when few racial minorities live in the community in which an organization is embedded, workforce diversity has an impact on employees' diversity climate perceptions. As racial minority popular share increases, workforce diversity tends to lose this signaling value.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.105
GPT teacher head0.344
Teacher spread0.239 · 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

Citations240
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PsychologySame topicGender Diversity and InequalityFrench-language works237,207