Bibliographic record
Abstract
The goal of this symposium is to further unpack the concept of stigma in relation to other types of social evaluation in the context of organizations and professions. Stigmatized actors are labeled by salient audiences as having a fundamental, deep-seated flaw (Devers, et al., 2009). This stigma discredits the actors, discouraging others from engaging with it. By discouraging engagement with the organization, the stigmatizing audience is able to exert social control over the stigmatized actor. Stigma is one of only a few negative social evaluations that have been examined in the management literature, and there is significant work to be done to both disentangle it from other evaluations and to build conceptual clarity around the intersections among various social evaluations. More specifically, we aim to make a first step towards understanding the differences and connections among the concepts of stigma, status, legitimacy, and reputation. Stigma and Status Hierarchies: Micro-Occupational Communities in the Sex Work Industry Presenter: Madeline Toubiana; U. of Alberta Presenter: Trish Ruebottom; Brock U. Organizational responses to stigma and social categorization processes Presenter: David Moura; Florida Atlantic U. Presenter: Bryant A. Hudson; IESEG School of Management The Cultural Contingency of Law: Evidence from the Introduction of the Automobile Safety Law Presenter: Simona Giorgi; U. of Bath Presenter: Massimo Maoret; IESE Business School When Stigma Doesnt Stick: Predicting Factors in Stigma Erosion Presenter: Karen Diane Walker Patterson; U. of New Mexico Presenter: Jo-Ellen Pozner; Santa Clara U.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.050 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".