Partnerships in Community-based Approaches to Achieving Sustainability: The Atlantic Coastal Action Program
Bibliographic record
Abstract
The Government of Canada believes that a healthy democracy requires the active engagement of its citizens in understanding the economic, social, and environmental issues faced by the nation. In the Atlantic Region, Environment Canada has been actively working, for more than a decade, on helping citizens achieve this integrated view and providing local communities with the means to develop their own visions of sustainability. In this regard, the Atlantic Coastal Action Program (ACAP) has been one of Environment Canada Atlantic Region’s greatest success stories. ACAP is a community-based program that promotes local leadership and action. For more than 13 years, ACAP activities have involved thousands of community residents working as volunteers in local and regional initiatives. Their successes include solving complex problems related to sewage treatment, toxic contaminants and water quality, building local capacity and, educating their communities on issues such as pollution prevention, monitoring, climate change, assessment and household hazardous wastes to name a few. By working as a partner with local communities rather than imposing decisions, Environment Canada has helped a diversity of communities to responsibly address environmental issues of “local interest.” When communities realize that they can solve some of their own problems, they are empowered and can directly (or indirectly) influence decision-makers and policy makers. All of the ACAP groups have experiences in collaborative ecosystem management that have influenced local and/or regional decision-making. This paper outlines a number of these experiences, describes ACAP and its process as well as ACAP’s influence within Environment Canada and the rest of the Atlantic Region.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".