Explorations into the Conundrums and Complexities of Workplace Mistreatment
Bibliographic record
Abstract
The proposed symposium explores critical gaps in the workplace mistreatment literature by examining employee maltreatment in novel contexts (e.g., substance abuse counseling, doctoral programs) and for unique social groups (e.g., cancer survivors, “cold” women). The papers also advance previous research by identifying mediators (e.g., organizational citizenship behavior, depersonalization) and moderators (e.g., substance abuse recovery status, interpersonal warmth) of the relationship between mistreatment and several under-researched outcomes (e.g., blood pressure, occupational advancement). Together, this collection of papers introduces both puzzles and intricacies to the existing research stream and, as such, promises to be both interesting and engaging.Workplace Harassment and Team (In)effectivenessPresenter: Jana L. Raver; Queen's U.Presenter: Ingrid C. Chadwick; Queen's U.An Examination of Withdrawl Reactions to Patient IncivilityPresenter: Taylor Elizabeth Sparks; U. of GeorgiaPresenter: Kerrin E. George; U. of GeorgiaPresenter: Katie Kincaid; U. of GeorgiaPresenter: Lillian Eby; U. of GeorgiaWorking in a Climate of (In)civility and Blood Pressure Presenter: Laura Lomeli; Texas A&M U.Presenter: Kathi Miner; Texas A&M U.Presenter: Mindy E. Bergman; Texas A&M U.Presenter: Ismael Diaz; Texas A&M U.Surviving the Hiring Process: Evidence of Interpersonal Discrimination in Hiring Cancer SurvivorsPresenter: Larry R Martinez; Rice U.Presenter: Craig D White; Texas A&M U., College StationPresenter: Kelly A Mover; Rice U.Presenter: Michelle R. Hebl; Rice U.Gender, Mistreatment, and Advancement in Blue and Pink Collar OccupationsPresenter: Jennifer L. Berdahl; U. of TorontoPresenter: Sue H Moon; Long Island U.Presenter: Ji-a Min; U. of TorontoPresenter: Alexander Garcia Muradov; 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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".