Racioethnicity, community makeup, and potential employees’ reactions to organizational diversity management approaches.
Bibliographic record
Abstract
We draw on the values literature from social psychology and the acculturation literature from cross-cultural psychology to develop and test a theory of how signals about an organization's diversity management (DM) approach affect perceptions of organizational attractiveness among potential employees. We examine the mediating effects of individuals' merit-based attributions about hiring decisions at the organization, as well as the moderating effects of their racioethnicity and the racioethnic composition of their home communities. We test our theory using a within-subject policy-capturing experimental design that simulates organizational DM approaches, supplemented with census data for the participants' home communities. Results of hierarchical linear modeling (HLM) analyses suggest that the manipulated instrumental value for diversity leads to higher perceptions of organizational attractiveness, in part through heightened expectations of merit-based hiring decisions. Further, the manipulated assimilative and integrative DM approach signals are positively related to organizational attractiveness and the effect of integrative DM is strongest for racioethnic minorities from communities with especially high proportions of Whites and Whites from communities with especially low proportions of Whites. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".