Setting the Temporal Boundaries of Work: An Empirical Study of the Nature and Scope of Labour Law Protections
Bibliographic record
Abstract
Labour law aims to protect the employee, defined as a person who performs work for an employer and is entitled to a wage. Labour law provides tools to distinguish between work time and personal time. However, in several emerging models of work organization, workers must increasingly be available beyond the time actually spent carrying out their work, whether they are at the workplace or elsewhere. It is not clear whether workers are then considered to be performing work and entitled to compensation. Nor is it clear whether these periods are taken into account in the maximum weekly or workday working hours, in the minimum daily and weekly rest periods provided for or when calculating overtime. It is also uncertain whether these workers are deemed to be at work. The various contemporary manifestations of the requirement for workers to be available leads to the question of what constitutes the span of work that is in fact legally regulated by labour law. The results of an empirical study conducted in 2014 (which includes a comprehensive analysis of judicial decisions and data collected from a Quebec administrative body) and an analysis of the debates surrounding the revision ot the European Working Time Directive lead on to a discussion of the capacity of labour law to protect workers who have to be available beyond the time actually required to carry out their work tasks.
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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 | 0.022 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".