Potential for Performance-Based Regulation in the Canadian Offshore Oil and Gas Industry
Bibliographic record
Abstract
After providing a background and comparative assessment of Performance-Based Regulation (PBR) inother offshore oil and gas sectors, the potential for similar application in Canada is discussed. The developments in these sectors have evolved from a prescriptive regulatory scheme to one that is more PBR based. In such a regime, the governing agency sets out objectives for industry performance that include design and operation objectives, as well as expectations for safety and environmental protection. It is then up to the individual company to develop a program as to how they propose to achieve these performance objectives, which is then submitted to the agency for review. The discussion centres on the overall compliance and improvements that have been realized by PBR regimes, and the efficiency of the government agencies. The scheme is intended to be more responsive to industry changes and requires more participation by the regulated companies than in prescriptive regimes. Overall objectives of PBR are to reduce the level of prescriptive measures imposed upon industry by government. while reducing exposure to the risks of offshore oil and gas exploration and development by placing the means ofmanaging the risk in the hands of the operators. The premise of PBR is that these operators are in a belter position to react to changes in technology and risk than are government agencies.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".