Organizational Stigma: Taking Stock and Opening New Areas for Research
Bibliographic record
Abstract
Abstract Since its introduction as a concept, organizational stigma has become central to explaining how organizations or industries become tainted, and how they overcome and manage such taint. In this introduction to the Special Issue on organizational stigma, we start by exploring the origins of the concept, providing basic definitions and reviewing the existing research on stigmatization, stigma transfer and experienced stigma. The papers in this issue flesh out our understanding of what causes organizational stigma and its implications at different levels. The remainder of this introduction takes stock of this recent work to explore future research opportunities around the micro‐ and macro‐foundations of organizational stigma, the links with scandals, controversies and other negative social evaluations and research methods. As the concept of organizational stigma reaches a new stage, we argue that its explanatory power can be harnessed to explore new and increasingly relevant phenomena and contexts.
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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 | 0.036 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.022 | 0.053 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".