What counts in making MPAs count: The role of legitimacy as a contributor to perceived MPA success in Canada. [graduate project].
Bibliographic record
Abstract
Marine protected areas (MPAs) are powerful management tools used worldwide for conserving marine species and habitats. Yet, many MPAs fail to achieve their management objectives because of shortfalls in understanding stakeholders’ perceptions on the level of legitimacy they afford to an MPA, which can negatively impact an MPA’s effectiveness. The purpose of this study was to determine the importance of various factors in shaping different stakeholders’ and managers’ perceptions on MPA effectiveness and the level of legitimacy they afford to an MPA. Interviews were conducted with various stakeholders from two coastal MPAs in Atlantic Canada: Musquash MPA in New Brunswick, and Basin Head MPA in Prince Edward Island. Results indicated that most factors for legitimacy are important to stakeholders for MPA effectiveness, however some differences in perceptions were evident between and within different stakeholder groups, and among stakeholders and managers. Consensus was shared across case studies on the importance of community leadership and the establishment of trust. A novel legitimacy framework, as well as a more refined suite of indicators vetted by stakeholders for obtaining MPA legitimacy are presented and recommended for use by MPA managers in establishing/assessing the legitimacy of Canada’s future coastal MPAs. The results of this research allow for an increased understanding of stakeholder perceptions of legitimacy and help to simplify the task Canadian MPA managers have of establishing legitimate and ultimately effective MPAs during their efforts to reach Canada’s national targets of having covering 10% of national oceans in MPAs by 2020.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".