Negotiating identity through idiosyncratic deals: Ethnocultural minority employees and workplace stigmatization
Bibliographic record
Abstract
Abstract Idiosyncratic deals (i‐deals) allow employees to bargain flexibility and development opportunities proactively. The i‐deals literature has mainly examined privileged individuals, overlooking minority employees. This conceptual paper focuses on i‐deals requested by ethnocultural minority employees to cope with discrimination and one‐size‐fits‐all organizational practices. We reason that self‐verification motives grounded in a central ethnocultural identity may lead them to request accommodative i‐deals (e.g. flexibility allowing for religious rites), whereas self‐enhancement motives grounded in a central work identity may prompt them to seek growth i‐deals (e.g. responsibilities reflecting their qualification). We theorize the consequences of i‐deals requests on stigmatization through three Othering mechanisms: (1) social dominance: supervisors may grant more accommodative or growth i‐deal based on their preference for egalitarian/hierarchical relationships among social groups; (2) social identity: accommodative/growth i‐deals may heighten/attenuate out‐group status, respectively; (3) ideal worker norms: accommodative/growth i‐deals may be construed as deviation/compliance from/with ideal worker norms, respectively. Thus, i‐deals' (de)stigmatization consequences vary according to which i‐deals are obtained and in which sequence; moreover, this relationship is moderated by work group climate. Our framework contributes to the i‐deals and EDI literature by theorizing i‐deals as identity negotiation, foregrounding identity as a driver of behaviours and explaining stigmatization through distinct Othering mechanisms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".