Bibliographic record
Abstract
In this session, we present three papers that together advance our understanding of the consequences of customer mistreatment of employees, and potential ways to mitigate the negative effects on employees of these difficult interactions. These studies all consist of field data collected from the service workforce using multiple designs (e.g. event level, cross- sectional, and tetradic collected at three points in time) from multiple sources of data including employees, supervisors and customers. Moreover, these studies are undertaken in different countries – China, the Phillipines, Canada, and South Korea adding the opportunity for discussion about cross-national difference in customer mistreatment of employees. The Role of Self-Esteem Threat in the Experience of Customer Mistreatment Presenter: Rajiv Amarnani; Australian National U. Presenter: Simon Lloyd D. Restubog; The Australian National U. Presenter: Prashant Bordia; The Australian National U. Customer Mistreatment and Employee Attributions: An Event Level Analysis Presenter: Yujie Zhan; Wilfrid Laurier U. Presenter: Xiaoxiao Hu; Old Dominion U. Presenter: Xiang Yao; Peking U. Presenter: Manuela Priesemuth; Wilfrid Laurier U. The Compensatory Effect of Supervisor Fairness in Predicting Employee Sabotage Toward the Customer Presenter: Daniel Skarlicki; U. of British Columbia Presenter: Danielle van Jaarsveld; U. of British Columbia Presenter: Ruodan Shao; City U. of Hong Kong Presenter: Young Ho Song; McGill U.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".