Introduction: A Dialog on Stigma Versus Legitimacy, and How They Relate to Organizations and Their Actors
Bibliographic record
Abstract
Recently, stigma research has reached an important threshold in management literature. With an increasing number of publications on the topic of stigma and related social evaluations, researchers run the risk of convoluting disparate concepts. At the same time, examining different components of a singular social evaluation can result in unique contributions that might be overlooked if not thoroughly unpacked. This dialogue presents two differing perspectives on the social evaluations of stigma and legitimacy. The authors discuss the merit of examining stigma as its own distinct construct and as a component of moral evaluation. The authors engage previous research to provide insights on the origins, antecedents, outcomes, processes and consequences of stigma from two different perspectives. Finally, established stigma researchers provide insight into the debate, drawing on previous research as well as their own foundational work.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".