Indigenous peoples and marine protected area governance: A Mi’kmaq and Atlantic Canada case study
Bibliographic record
Abstract
This research examines the potential challenges and opportunities for Mi’kmaq, the Indigenous peoples who have inhabited modern-day Nova Scotia and other areas of Eastern Canada for millennia, to play a greater role in marine protected area (MPA) governance in Canada. Given Canada’s marine conservation objectives of 30% by 2030, there is a growing need for decisions affecting the establishment of MPAs to respect Indigenous rights, values, and knowledge. Using the Eastern Shore Islands (ESI) in Nova Scotia, Canada, an area of interest for MPA establishment, as a case study, we conducted 17 semi-structured interviews with both Mi’kmaq and non-Mi’kmaq participants involved in the ESI consultation processes. We used content analysis to identify key themes that respondents perceived to be affecting Mi’kmaq involvement in the federal MPA governance processes. Barriers to overcome included those deemed to be systemic within the current decision-making processes; limited understanding of Mi’kmaq culture, governance, and rights; limited clarity of Mi’kmaq rights, particularly those resulting in fisheries conflicts; and limited capacity. Opportunities highlighted the importance of meaningful consultation and understanding of Indigenous worldviews as well as the need for alternative approaches to state-led/top-down governance to improve Mi’kmaq participation in MPA governance in Atlantic Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.029 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".