Invisible Identity in the Workplace: Intersectional Madness and Processes of Disclosure at Work
Bibliographic record
Abstract
In this article I draw on recent work regarding disabilities that are not readily apparent to analyze the experiences of lesbian, gay, bisexual, queer, and/or trans (LGBQT) mad people in the workplace. Based on interviews with LGBQT people about madness and everyday life, I use an intersectional approach to examine participants’ work lives. I argue that decisions about disclosure of mental health related information are particularly pressing and high risk at work, given the economic stakes and the effects on health and well-being. As is the case for others with invisible disabilities, notions of authenticity shape processes of disclosure and access to accommodations for LGBQT mad people in the workplace. An intersectional analysis shows how madness cannot be considered the only salient aspect of my participants’ subject positions and how multiple identities operate together to shape their experiences. Keywords: madness, invisible disability, LGBQT, intersectionality, workplace, race, sexuality, gender identity
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".