Stigma and Legitimacy Loss: Professions, Social Judgments, and Symbols in Crime and Punishment
Bibliographic record
Abstract
In this symposium we present five papers focused on how the judgments of external stakeholders affect the apportionment of the consequences of misconduct. The papers included attack the question in various settings and across levels of analysis, ranging from teachers’ personal comportment to members’ of Parliament misspending to corporate fraud. Yet each paper investigates how social control agents - including the media, institutional investors, stock analysts, and other audiences - draw inferences about the seriousness and severity of infractions, and how these inferences affect the punishments that are assigned to various actors involved. This symposium will generate conversation about both theoretical and empirical questions related to misconduct and ethics, stakeholder relationships, and governance. Pragmatic or Moral Legitimacy: Effect of Director Capabilities versus Nonprofit Ties on Punishment Presenter: Daphne Teh; INSEAD Repeat Offenders: How The Consequences of Firm Misconduct Abate Across Incidents Presenter: Celia Moore; London Business School Presenter: Aharon Yehuda Cohen Mohliver; London Business School Presenter: Jo-Ellen Pozner; Santa Clara U. The Process of Scandal Formation and the Role of Social Control Agents Presenter: Timothy R. Hannigan; U. of Alberta Presenter: James B. Wade; George Washington U. Presenter: Joseph Porac; New York U. The Professional Consequences of Misconduct Presenter: Jacob Model; Stanford U. Corporate Misconduct and Heterogeneity in the Reputational Penalties to Managers and Directors Presenter: Ivana Naumovska; INSEAD Presenter: Georg Wernicke; Copenhagen Business School
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".