Let’s Not “Taint” Stigma Research With Legitimacy, Please
Bibliographic record
Abstract
We propose that stigma and legitimacy are distinct constructs. Drawing from extant research, empirical observations, and the theoretical assumptions of both constructs we assert that, in spite of increasing efforts to equate stigma as illegitimacy, the opposite of legitimacy, that it is not. Specifically, we argue that organizations and their actors can be both stigmatized and legitimate at the same time. With this recognized, we propose a stigma-focused research agenda, separate from - and untainted by - legitimacy. Further, we propose an agenda that broadens conceptualizations of audiences and their dynamics, addresses how normal “deviants” take action in the face of stigma, and reconceptualises how audiences and the stigmatized interact.
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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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.011 | 0.032 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".