The Evolution of the Frontline Employee Role: Interdisciplinary Perspectives and the Road Ahead
Bibliographic record
Abstract
The goal of this presentation symposium is to highlight the importance of understanding the evolution and dynamics of the contemporary frontline employee (FLE) role. Through the incorporation of FLE-oriented papers across a diverse range of disciplines, this symposium will contribute to the AOM 2024 conference themes of 'innovating for the future' by discussing new directions in FLE role-related research, providing insights into how to help FLEs better manage changes in their line of work, and building bridges between various disciplines engaged in understanding this phenomenon. Crystal Clear: How Leaders and Coworkers Shape Role Clarity and Well-Being for Social Care Employees Author: Nate Zettna; The U. of Sydney Author: Cheryl Yam; Curtin U. Author: Arian Kunzelmann; Future of Work Institute, Curtin U. Author: Vivien W. Forner; U. of Wollongong Author: Shanta Dey; U. Of Sydney Author: Mina Askovic; U. of Sydney Business School Author: Anya Madeleine Johnson; U. Of Sydney Author: Anupama Jolly; Curtin U. Hospitals as Total Institutions: Bureaucratic Control vs Patients' Psychological Needs Author: Dana Yagil; U. of Haifa Author: Smadar Weinstein; U. of Haifa Downstream Effects of Hospital Staff Emotional Exhaustion and the Moderation of Patient Orientation Author: Helena Nguyen; U. Of Sydney Author: Joseph Carpini; U. of Western Australia Author: Anya Madeleine Johnson; U. Of Sydney Author: Aleksandra Luksyte; U. of Western Australia A Gender Discrimination Perspective on Customers' Perceived Customer Orientation and Incivility Author: Rajiv Amarnani; U. of Western Australia Author: Yumeng Yue; U. of Edinburgh business school The Evolution of Research on Frontline Employee Experiences and Responses to Customer Mistreatment Author: Su Kyung Kim; U. of Manitoba Author: David Douglas Walker; U. of British Columbia
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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 | 0.039 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".