Knowledge Translation Supports Community Conservation Efforts to Protect and Restore a Local Marine Environment: A Case Study of Átl’ḵa7tsem/Txwnéwu7ts/Howe Sound, British Columbia, Canada
Bibliographic record
Abstract
Individuals, communities, organizations, and governments are the building blocks of economies. All require awareness, information, and achievable actions to contribute to moving towards healthy oceans, the base of a robust blue economy. Ocean Watch, a program run by Ocean Wise Conservation Association, was created to translate scientific understanding, combined with community and traditional knowledge, to empower local action for improved marine health. Clear improvements have been made in the Átl’ḵa7tsem/Txwnéwu7ts/Howe Sound marine environment, which have been captured in an updated report following from the original 2017 publication. Information within the reports illustrates the connection of communities and the marine environment through articles describing seven themes, which are: (1) Species and Habitat; (2) Clean Water; (3) Sense of Place; (4) Coastal Development and Livelihoods; (5) Stewardship and Governance; (6) Oceanography and Climate Change; and (7) Seafood. Articles such as the 2017 article: “Sea Stars: wasting disease taking its toll” gave background, rationale for importance, current status, current actions, and recommended actions to improve the health status (healthy, caution, critical, limited/no data). The health status for 10 of 28 reassessed articles improved largely due to actions taken by local communities, as recommended in the 2017 report. However, more work is needed, especially for areas of marine health that were not improving and to address threats from climate change. Establishing a sustainable socio-ecological relationship with the ocean is necessary if we are to protect and restore the health of all components of the ecosystem. Empowering communities to take action improves ocean health, which is inherently linked to the health of individuals, communities, and economies.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.035 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".