Expressing religious identities in the workplace: Analyzing a neglected diversity dimension
Bibliographic record
Abstract
Responding to Jackson and Joshi’s (2011) call for specific models of the effects of particular diversity types and against the backdrop of the rising desire for the public expression of religious identities in the workplace (Hicks, 2003), we develop a framework that systematically explores when and how the expression of diverse religious identities induces relational conflicts in organizational units. In developing this framework, we integrate the respective literatures on religion studies (e.g. Hicks, 2003), identity-disclosure (e.g. Ragins, 2008) and diversity within organizational groups (Jackson and Joshi, 2011). Our framework specifies three paths whereby the public expression of diverse religious identities can engender relational conflicts. As mediators, we discuss perceivers’ attribution of proselytism and religious discrimination, as well as identity threats. Moreover, we examine the moderating roles of actors’ and perceivers’ religious fundamentalism, perceivers’ religious identity salience and minority members’ attribution of majority members’ religious hegemonial claims. At the theoretical level, we delineate particularities of religious identity diversity that distinguish this diversity type from other deep-level diversity attributes. Concerning practical implications, we argue that it is important to not only foster self-expression, but also to be cognizant of the risks that the public expression of religious identities entails.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".