Working Together on a Coastal Marine Strategy for British Columbia.
Bibliographic record
Abstract
On the coast of British Columbia (BC), the effects of climate change, pollution, seafood production, coastal development, and transportation on our environment and related values are increasing. In addition, historical inequities in terms of access to ocean resources, the distribution of power and benefits, and exposure to harms persist and in some cases are exacerbated by business-as-usual approaches to marine management. Ensuring sustainable and equitable development of our marine economy in BC is possible with better, more complete ocean governance and strategies that reflect current and future values and pressures. As one of only a few maritime jurisdictions in North America that has not yet developed a strategic framework to manage our coasts and ocean, the province’s recent commitment to build a Coastal Marine Strategy is expected to close a significant gap in BC’s ocean-related policy. Ocean management within BC’s coastal waters is complex, involving all levels of government and requiring deep consultation with coastal First Nations. The Declaration on the Rights of Indigenous Peoples Act, signed into provincial law in November 2019, creates a path forward that respects the human rights of Indigenous peoples while introducing better transparency and predictability in the work we do together. This presentation relates to the topic of governance and will share how the province and coastal First Nations intend to work together on improving stewardship of the ocean and social equity through the development of the Coastal Marine Strategy. This presentation will be of particular interest to other jurisdictions and practitioners who are working on the development of strategic policy that requires multi-jurisdictional collaboration and seeks to address place-based issues and opportunities at a coast-wide scale.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".