(De)Stigmatization and The Inclusive Organization
Bibliographic record
Abstract
This symposium showcases recent work about the processes of stigmatization and potential for destigmatization in organizations. Five papers ranging from empirical to conceptual discuss how and why different types of employee identities and characteristics are stigmatized and the implications of that stigmatization. The papers investigate a diverse set of stigmas non-native accents, sexual minority status, mental illnesses, cumulative stigmas, and stigma by association and a diverse array of outcomes: entrepreneurial funding decisions, coworker treatment, trust, psychological outcomes, and hireability. Further, each paper provides insight what factors may increase or decrease these stigmatization processes. Together, the papers of this symposium fulfill two goals: 1) provide deeper explanations as to how and why stigmatization occurs; and 2) shed light on the role destigmatization can play in making organizations more inclusive. Effects of Disclosure on Evaluations of Nonnative Speakers and Entrepreneurial Investment Decisions Presenter: Regina Kim; IESEG School of Management Presenter: Rae Yunzi Tan; U. of Baltimore Heterosexual Employees’ Identity Threat Responses to Gay/Lesbian Disclosure Presenter: Brent John Lyons; Schulich School of Business Presenter: John Lynch; U. of Illinois at Chicago Presenter: Tiffany Dawn Johnson; Georgia Institute of Technology A Continuum of Workplace Mental Health and Illness and its Relationship with Leader Trust Presenter: Amanda J. Hancock; Memorial U. of Newfoundland Presenter: Kara Anne Arnold; Memorial U. of Newfoundland Validation of a Stigma Load Instrument: Implications for Developing an Inclusive Organization Presenter: Roxanne Beard; McKendree U. Presenter: Robyn A. Berkley; Southern Illinois U., Edwardsville Presenter: Catherine Daus; Southern Illinois U., Edwardsville Destigmatization and Its Imbalanced Effects in Labor Markets Presenter: Giacomo Negro; Emory U. Presenter: Melissa J. Williams; Emory U. Presenter: Elizabeth Pontikes; UC Davis
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".