Managing Work Scheduling in Organizations: Creating Positive Dynamics
Bibliographic record
Abstract
Given the need for more research on work scheduling to match employee and employer interests, the goal of this symposium is to provide a new look on work scheduling to advance understanding theoretically and empirically. The symposium fits well with the 2018 theme of the AOM conference that focuses on how organizations can contribute to the betterment of society. This symposium suggests that how work schedules are constructed and managed can affect outcomes for patients and workers in long term health care; attributions about career commitment, stigma and performance; labor costs to match diverse employees’ and business needs; and work-life stress across global time zones. Managing Work Schedule Uncertainty and Justice in Health Care: Schedule Patching Strategies Presenter: Ellen Ernst Kossek; Purdue U. Presenter: Lindsay Mechem Rosokha; Purdue U. Are Flexible Working Policies Riskier to Use Than Other Work- Life Policies? Career Consequences Presenter: Sarah Bourdeau; UQAM U. du Québec A Montréal Presenter: Ariane Ollier-Malaterre; UQAM U. du Québec A Montréal Presenter: Nathalie Houlfort; U. du Québec à Montréal (UQAM) Managerial Incentives, Decisions, and Outcomes: A Quasi-Experiment Impacting Labor Scheduling Presenter: Saravanan Kesavan; Harvard U. Presenter: Kuhnen Camelia; UNC Presenter: Hyun Seok Lee; Oregon State U. Scheduling Work Across Time Zones: Impact on Global Employees' Stress Presenter: Kaumudi Misra; California State U. East Bay
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".