How supervisors set the tone for long hours: Vicarious learning, subordinates' self-motives and the contagion of working hours
Bibliographic record
Abstract
This paper develops a theoretical model that highlights the mechanisms underlying the contagion of long working hours from supervisors to subordinates at different stages of their relationship. Drawing upon social learning theory, we suggest that subordinates mimic the supervisor's working hours through vicarious learning. Focusing first on the role-taking stage of the supervisor-subordinate relationship, we identify four factors, namely supervisor's perceived status, subordinate's work centrality, congruence between organizational norms and supervisor's working hours, and subordinate's identification with the supervisor, that may influence the perceived desirability of adopting the supervisor's working hours. We then examine the relative influence of each of these factors through the lens of subordinates' self-motives. Turning, next, to the routinized supervisor-subordinate relationship, we elaborate on how social contagion may evolve over time. Lastly, the implications of our model as well as future research avenues are presented.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".