Revisiting Integrated Coastal and Marine Management in Canada: Opportunities in the Bay of Fundy
Bibliographic record
Abstract
Limited progress has been made in implementing integrated coastal and marine management (ICM) policies globally. A renewed commitment to ICM in Canada offers an opportunity to implement lessons from previous efforts over the past 20 years. This study applies three core ICM characteristics identified from the literature (formal structures; meaningful inclusion; and, innovative mechanisms) to identify opportunities for operationalizing ICM from participants’ lived experiences in Atlantic Canada. These characteristics are employed to assess and compare ICM initiatives across two case studies in the Upper Bay and the Lower Bay of Fundy. The assessments are based on semi-structured interviews conducted with key participants and a supplementary document analysis. The following insights for future ICM policies were identified: adaptive formal structures are required for avoiding previous mistakes; a spectrum of approaches will support meaningful engagement in ICM; local capacity is needed for effective innovative mechanisms; and, policy recommendations should be implemented in parallel. Although these insights are relevant to each of the two sub-regional case studies, the paths taken to incorporating and realizing them appear to be location-specific. To account for these site-specific differences, we suggest more attention be given to strategies that incorporate local history, unique capacity of actor groups and location-specific social-ecological systems objectives. We provide the following recommendations on policy instruments to assist in moving toward enhanced regional ICM in the Bay of Fundy, and that may also be transferable to international ICM efforts: update policy statements to incorporate lessons from previous experiences; strengthen commitment to ICM in Federal law; create a regional engagement strategy to enhance involvement of local actor groups; and, enhance the role of municipal governments to support local capacity building and appropriate engagement of local actors in ICM processes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".