Bibliographic record
Abstract
Since the advent of the Industrial Revolution, working time has become the central focus of productive organizations. However, the heterogeneity of employment status has transformed the nature of a homogeneous and uniform working time by introducing unpredictability. It is not clear whether labour regulation can decipher this reality in all of its dimensions, or whether it offers adequate protection. Our research will focus on the Canadian regulatory framework for working time among wage-earning unionized truck drivers in the for-hire trucking industry at the international or interprovincial level. After first examining the formal rules and their case law interpretation, we will seek to better understand the obligation to be available by using the field survey method, conducted in 2014, which is essential to ‘grasp law and social practices in their interactive dynamics’. The interviews reveal that, despite extensive regulation, drivers are nevertheless subject to an obligation to be available, which can be explained by the latitude the actors have in interpreting or using the formal statement of rules, a latitude that contributes to deteriorating working conditions among truck drivers. Thus, at the margins of formal labour law, a substantial informal normative system has developed which generates this third time.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".