Identity management in the workplace: Coworker perceptions of individuals with contested disabilities
Bibliographic record
Abstract
BACKGROUND: Much of the existing research on disability and disability-related workplace accommodations presume that disabilities are visible and commonly accepted. Yet, many disabilities are invisible and contested, or perceived as fake, low-severity/minor, and/or illegitimate. OBJECTIVE: The purpose of this research is to investigate the effect of identity management strategies that individuals with contested disabilities might use when requesting accommodations in a workplace setting. METHODS: We used two electronic experiments to investigate the effect of identity management strategies on perceived fairness of accommodations and attributions about individuals requesting accommodations. Studies 1 and 2 used online surveys to collect data from 117 and 184 working adults, respectively. RESULTS: Study 1 indicates that four invisible disabilities (chronic fatigue syndrome, attention deficit/hyperactivity disorder, generalized anxiety disorder, and chronic migraine) are viewed as significantly less legitimate than the visible disability paraplegia. In study 2, any form of disclosure of a contested disability (vs. no disclosure) resulted in higher perceived fairness and more positive attributions about the person requesting accommodations. There were minimal differences between the different identity management strategies tested. CONCLUSIONS: Workplaces should work to create spaces in which employees can disclose contested disabilities to managers and coworkers without fear of enhanced stigmatization.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".