Proactive Perspectives on the Service Frontlines: From Managing Mistreatment to Enhancing Civility
Bibliographic record
Abstract
The aim of this presentation symposium is to highlight the importance of understanding, managing, and mitigating the effects of interpersonal mistreatment (i.e., from customers or coworkers) on employees, customers, and the service workplace. This symposium will foster discussion among scholars from multiple disciplines (e.g., HR, OB, Occupational Health, Psychology, Marketing), with different theoretical and methodological perspectives, who conduct mistreatment research in multiple national contexts (Australia, Canada, Germany, and USA). By doing so, we aim to identify potential directions for the advancement of theoretical understanding, empirical research, and managerial policy focused on managing mistreatment and enhancing civility on the service frontlines. Customer Mistreatment in Service and Healthcare Encounters: A Bibliometric Review Author: David Douglas Walker; The University of British Columbia Author: Su Kyung Kim; University of Manitoba Author: Danielle Van Jaarsveld; The University of British Columbia Let Me Speak to Your Manager! Employee Responses to Leader Loyalty During Customer Mistreatment Author: Stephen M Reid; University of Iowa Author: Stephen Hyrum Courtright; University of Iowa Violence and Negative Behavior as Distinct Emotional Labor Demands: Implications for HRM in Service Author: Andrea Fischbach; German Police University Time Out: Using Breaks to Halt Cycles of Mistreatment Author: Karyn L. Wang; The University of Sydney Author: Anya Madeleine Johnson; The University of Sydney Author: Stefan Volk; The University of Sydney Quick Team Formation: How Presumptive Trust Enhances Coworker-Directed Citizenship Behavior Author: Nicholas A. Smith; Northern Illinois University Author: Mahesh Vaidyanathan Subramony; Northern Illinois University Author: Ilias Danatzis; King's College London Author: Lisa Van Der Werff;
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".