Bibliographic record
Abstract
Is there room for Arctic Ocean coastal states to harmonize how they regulate offshore oil and gas activity in the Arctic Ocean? This article answers yes, that coastal state authorities responsible for regulating offshore activity can work incrementally toward convergence in matters they identify as best suited to harmonization. Canada and the United States are the focal points of this study. Their shared maritime boundary in the Beaufort Sea traverses a part of the Arctic Ocean that is poised for expanded oil and gas exploration. Even if the boundary were not the subject of a well-managed diplomatic disagreement, regulators would do well to consider how their practices might complement and benefit both states. Possible advantages range from efficiencies in sharing baseline geo-physical, oceanographic or biological information, to combining resources for oil spill response planning. Such bilateral discussions can also provide lessons for offshore oil and gas regulators throughout the circumpolar north.In the wake of the April 2010 Deepwater Horizon/BP Macondo explosion and blowout in the Gulf of Mexico, regulators around the world began reviewing their respective approaches to offshore regulation. This paper draws in part on the results of those studies, in part on US and Canadian executive and legislative provisions, and on other literature.The Appendix contains a partial listing of Forums and Studies Relevant to Arctic Offshore regulation through June 2012.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".