Exploring Risk Governance Deficits for Marine Oil Spill Preparedness and Response in Canada
Bibliographic record
Abstract
Abstract Preparedness for and response to marine oil spills are important for protecting the Canadian marine areas, as these risks can have significant environmental, economic, and socio-cultural impacts. The vast sea areas under Canadian jurisdiction, combined with the wide range of maritime activities taking place in these, pose significant challenges to efficient preparedness and response planning and operation. The multitude of national and international regulatory commitments, rightsholder and stakeholder interests, and prospects of changes to shipping activities especially in the Canadian Arctic due to climate change justifies the need for effective societal risk governance and risk management. This chapter first outlines the regulatory context and governance practices for spill preparedness and response in Canada, focusing on the legal basis, responsibilities of different actors, engagement activities with rights- and stakeholders, and decision-making processes. It then highlights how these measures can be understood as an implementation of area-based management tools to mitigate oil spill risks. Subsequently, risk governance deficits in the preparedness and response governance and management systems are explored through interviews with experts from federal civil services, based on commonly found deficits identified by the International Risk Governance Council. The results indicate that the main deficits pertain to factual knowledge about risks, evaluating risk acceptability, implementing and enforcing risk management decisions, organizational capacity for risk management, and handling dispersed responsibilities. The results serve as a basis for developing initial strategies for alleviating the deficits, improving oil spill preparedness and response and environmental protection, and guiding further scholarship.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".