When is Enough, Enough? Insights on Career Implications of Being Treated Unfairly at Work
Bibliographic record
Abstract
Despite the prevalence of individuals experiencing unfairness at work in many different forms across the globe, we know little about how being treated unfairly has implications for one’s career success. Without this knowledge, we are ill equipped to advise individuals who are treated unfairly on best practices for managing their situations in ways that will limit potential damage to their careers. This symposium includes three papers, each of which investigates mistreatment or unfairness at work within a specific context: abusive supervision, the 'dark' side of friendship, and knowledge theft. We integrate these contexts here with two central objectives: 1) to understand how exposure to mistreatment or unfairness affects individuals’ career-related outcomes, and 2) to understand when, why, and how individuals should act to effectively manage their experiences. Importantly, all papers highlight some of the perceived career-related costs associated with acting or failing to act when responding to unfairness or mistreatment at work. By providing insight on the mechanisms involved with action or inaction, we contribute new knowledge to the field that will inform individuals on how to protect their careers when they are exposed to mistreatment or unfairness in the workplace. When is Enough, Enough? Insights on Career Implications of Being Treated Unfairly at Work Presenter: Paulien D’Huyvetter; KU Leuven Presenter: Marijke Verbruggen; KU Leuven Presenter: Gina Gaio Santos; School of Economics and Management, U. of Minho, Braga, Portugal Presenter: Ryan M. Vogel; Fox School of Business, Temple U. Presenter: David Zweig; U. of Toronto Presenter: Alycia Marie Damp; U. of Toronto
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".