When the Masks Come Off: The Initiation and Aftermath of Disclosure Decisions
Bibliographic record
Abstract
This symposium focuses on the challenges and complexities of disclosure decisions for individuals with stigmatized identities. These papers examine the issue from multiple perspectives, including both the person making the disclosure decision as well as the person(s) who prompt and/or receive this information. The symposium brings together scholarship on LGBTQ+ employees, individuals with disabilities, as well as women with criminal records. Further, the symposium will explore the boundary between the disclosure of a stigmatized identity from information that is more generally private. Additionally, the papers in this symposium utilize an array of research methodologies including surveys, policy-capturing, and diary studies. Dear Diary: How I feel after disclosing my stigma Author: Robyn A. Berkley; Southern Illinois University, Edwardsville Author: David Kaplan; Saint Louis University Author: Catherine Daus; Fit to Lead: Sexual Orientation Disclosure & Perceptions of Leadership Suitability in the Military Author: Brandon Legacy; Queen's University Author: Eddy S. Ng; Queen's University Navigating Disclosure Event Disruption: Relational Outcomes & Psychological Safety in Org, Context Author: Toschia M. Hogan; Saint Louis University Hiring with Conviction: A Review of Stigma Encountered for Women with Criminal Backgrounds Author: Marionne Sevilla; Prompting Disclosures: How Employees React to Personal Inquiries in the Workplace Author: John Lynch; University of Illinois at Chicago
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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".