Disclosing a disability: Do strategy type and onset controllability make a difference?
Bibliographic record
Abstract
In hiring contexts, individuals with concealable disabilities make decisions about how they should disclose their disability to overcome observers' biases. Previous research has investigated the effectiveness of binary disclosure decisions-that is, to disclose or conceal a disability-but we know little about how, why, or under what conditions different types of disclosure strategies impact observers' hiring intentions. In this article, we examine disability onset controllability (i.e., whether the applicant is seen as responsible for their disability onset) as a boundary condition for how disclosure strategy type influences the affective reactions (i.e., pity, admiration) that underlie observers' hiring intentions. Across 2 experiments, we found that when applicants are seen as responsible for their disability, strategies that de-emphasize the disability (rather than embrace it) lower observers' hiring intentions by elevating their pity reactions. Thus, the effectiveness of different types of disability disclosure strategies differs as a function of onset controllability. We discuss implications for theory and practice for individuals with disabilities and organizations. (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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".