Stigma Beyond Levels: Advancing Research on Stigmatization
Bibliographic record
Abstract
Stigma has become an increasingly significant challenge for society. Recognition of this problem is indicated by the growing attention paid to it within the management literature, which has provided illuminating insights. However, stigma has primarily been examined at a single level of analysis: individual, occupational, organizational, or industry. Yet, cultural understandings of what is discreditable or taboo do not come from the individual, occupation, organization, or industry that is stigmatized; on the contrary, they come from particular sources that transcend levels. As such, we propose that current silos within the literature may not only be preventing engagement with insights from different levels of analysis but, importantly, be preventing us from truly understanding stigmatization as a social process. To address this issue, we review the stigma literature and then present a cross-level integrative framework of the sources, characteristics, and management strategies therein. Our framework provides a common language that integrates insights across these levels and enables a shift in attention from how actors respond to stigma to broader processes of stigmatization.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".