Bibliographic record
Abstract
This article examines Bill C-45 and its possible impact on the Canadian Petroleum Industry. Bill C- 45, An Act to amend the Criminal Code, establishes a new occupational health and safety (OHS) duty in the Criminal Code. If the duty is breached, either by individuals or organizations, it may result in a criminal charge of occupational health and safety criminal negligence. Bill C-45 also changes the means by which organizations, including corporations, are held liable for offences under the Criminal Code. The "identification theory" of corporate criminal liability has now been replaced by two provisions in the Criminal Code to address the criminal liability of organizations. The first provision, which deals with offences such as the new OHS criminal negligence offence, requires proof of negligence. The second deals with a more classic objective fault element or mens rea. The Canadian petroleum industry, especially in Alberta, has often relied upon outsourcing, contract provisions and structuring of relationships to minimize OHS legal liability under applicable OHS statutes. However, there is no legal basis to contract out of the Criminal Code. It is more important than ever to emphasize proper contract language, indemnity provisions and OHS management systems.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".