Perceiving and Disrupting Discrimination in Organizations
Bibliographic record
Abstract
Despite the increasing demographic diversity in the workforce, discrimination against marginalized social groups remains a prevalent issue for many modern organizations (Hebl et al., 2020). However, the nature of discrimination is constantly evolving. While overt and explicit animus is often taboo, biases continue to creep into daily interactions in subtler ways and seep into the fabric of organizations in a manner that can be harder to detect. These challenges lead to questions about how discrimination can be overcome. In some cases, individuals who are subjected to discrimination respond at an individual level through confronting biased behaviors (Czopp & Monteith, 2003) or pushing back on organizational practices (Meyerson & Scully, 1995). Marginalized employees also attempt to engage in collective action with similarly situated individuals who are motivated to push back on the status quo (Scully & Segal, 2002). Finally, enlisting the help of allies from historically advantaged groups can be another tactic to shift the system (Droogendyk et al., 2016). While each of these approaches to fighting discrimination has promise, the present symposium investigates the obstacles that prevent these tactics from reaching their full potential. Drawing upon multiple methods (qualitative, survey, and experimental) and theoretical lens (identity management, collective action, allyship, intersectionality), we highlight issues in each of the above domains of individual action, collection action, and allied action. Collectively, these studies shed light on the experiences of employees navigating and responding to discrimination in the workplace, the barriers that undermine these efforts, and what may offer hope of effectively disrupting organizational discrimination and inequality. Navigating Race at Work: A Two-Dimensional Framework of Minority Racial-Identity Management Presenter: Rachel Arnett; The Wharton School, U. of Pennsylvania Presenter: Keana Richards; U. of Pennsylvania Presenter: Serenity Lee; The Wharton School, U. of Pennsylvania Presenter: Patricia Faison Hewlin; McGill U. Sanctioned Radicals: Comparing employees’ collective organizing around race versus gender Presenter: Lumumba Seegars; Harvard Business School The Divergence Between Descriptive and Prescriptive Expectations for Gay Men and Lesbian Women Presenter: Sa-kiera Hudson; Yale U. Presenter: Asma Ghani; Harvard U. Presenter: Aerielle Allen; New York U. Marginalized Workers’ Allyship Perceptions of Workplace Friends and Managers Presenter: Chade Darby; Cornell U. Presenter: Courtney Lynn McCluney; ILR at Cornell
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".