Disconnected Disclosures: Employee-Manager Asymmetries in Navigating Invisible Disabilities
Bibliographic record
Abstract
Despite the ubiquity of invisible disabilities (IDs), workplace discrimination towards employees who have them remains pervasive. Consequently, the act of disclosing these stigmatized disabilities is often fraught with professional risk. This research investigates the disclosure strategies that employees with IDs tend to adopt and whether they actually mitigate bias and maximize managerial support. Drawing from stigma theory and signaling theory, I propose two dimensions of invisible disability disclosure strategies: transparency (the amount of information provided); and activeness (the extent of accommodations requested). I theorize a critical disconnect between the disclosure strategies that employees tend to use and those which foster positive managerial reactions. Six studies employing correlational and experimental designs with diverse online and field samples investigate this asymmetry between employees and managers, and its critical implications for employees with IDs. The results demonstrate that to avoid stigma, employees tend not to provide details nor to request accommodations, when in fact strategies that are both transparent and actively request accommodations counteract stereotypes and enhance managerial support. Counterintuitively, by ‘owning’ their stigmatized identities and requesting clear solutions, employees with IDs can increase managerial perceptions of ability and the likelihood of receiving accommodations following disclosure.
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 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 | 0.024 | 0.082 |
| 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 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".