Bibliographic record
Abstract
The paper discusses how, in their original form, the Hours of Service (HOS) regulations were as much a labor issues as a safety issue. The need for adequate rest was recognized, along with the need to make a distinction between driving and non-driving in order to prevent drivers from spending too many house behind the wheel. Transport Canada first introduced a Canadian version of the rules in 1984. Similar to the American rules, the Canadian law offers more latitude with respect to duty cycles, and longer hours behind the wheel. Both countries require drivers to log certain activities as on-duty time, but over the years much of that prescribed has slipped of the pay ledger. For reasons unknown, drivers never objected too strenuously to this, and as a result, it is now common practice for carriers not pay drivers for this time. However, drivers today are feeling the pinch of this unpaid time. Record-breaking delays at major border crossings are causing considerable stress for drivers who once made up that lost time by hiding the wasted hours in the logbook, and running a few extra miles to make up the difference. The situation will only worsen until regulators re-examine the HOS laws from both a labor and an economic perspective, rather than solely from a safety perspective.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".