From Apples and Cases to Barrels and Orchards: Macro-Level Drivers of Workplace Abuse
Bibliographic record
Abstract
Workplace abuse, broadly defined as interpersonal mistreatment against employees in the workplace that might harm or injure them and contribute to a hostile work environment, is one of the most pervasive and harmful problems faced by organizations worldwide. In the current symposium, we focus attention on the macro-level drivers of workplace abuse that occur within organizations and in society more generally. At the societal level, we will have one paper about important global trends affecting today’s organizations. The paper investigates how, why, and for whom these macro forces have implications when it comes to workplace harassment. At the organizational level, we will have three papers, one dedicated to unpacking the multiple dimensions of organizational tolerance for abuse. Two more papers will focus on structural organizational features, namely the mechanisms of communication, and structural pay inequality that can impact perceptions of interpersonal abuse at work. We argue that a stronger focus on studying the “barrel” and “orchard”, rather than “apples” and “cases”, can enhance our understanding of social and structural factors that underpin everyday workplace interactions and help us identify new avenues of theorizing and practice to prevent interpersonal workplace abuse. Workplace Harassment in the Larger Social Context - A Function of Our Times Presenter: Jana L. Raver; Queen's U. Presenter: Ingrid Chadwick; Concordia U. Presenter: Xiaoxi Chang; Smith School of Business, Queen's U. Organizational Tolerance and Non-Accidental Violence in Sport - A Systematic Review Presenter: Victoria Louise Roberts; U. of Melbourne Presenter: Victor Sojo Monzon; Centre for Workplace Leadership, The U. of Melbourne Presenter: Felix Grant; U. of Melbourne Reply to All - A Content Analysis of Email Incivility Presenter: David Jay Howard; U. of South Florida Presenter: Cheryl Gray; U. of South Florida Presenter: Logan Macray Steele; U. of South Florida Presenter: Paul E Spector; U. of South Florida Pay Disparity, Leader-Member Exchange and Incivility - A Contextual Approach Presenter: Tine Koehler; U. of Melbourne Presenter: M. Gloria Gonzalez-Morales; U. of Guelph Presenter: Jose M. Cortina; Virginia Commonwealth U. Presenter: Jesse E. Olsen; U. of Melbourne Presenter: Adrienne O'Neil; U. of Melbourne Presenter: Adriana Vargas-Saenz; U. of Melbourne Presenter: Rebecca Schachtman; U. of Melbourne Presenter: Elise Holland; U. of Melbourne Presenter: Victor Sojo Monzon; Centre for Workplace Leadership, The U. of Melbourne
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".