What counts in making marine protected areas (MPAs) count? The role of legitimacy in MPA success in Canada
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 the level of legitimacy stakeholders afford to an MPA. Legitimacy refers to the ability of a political action, in this case an MPA, to be perceived as right and just by the various people who are involved, interested, and/or affected by it. Using responses from key stakeholders and managers at two coastal MPAs in Atlantic Canada, this study examined the importance of various factors shaping perceptions of MPA effectiveness and the role of legitimacy in influencing those perceptions. Results indicate that most indicators of legitimacy are important to stakeholders for MPA effectiveness. Specifically, there was consensus across case studies on the importance of community leadership and the establishment of trust in determining the level of legitimacy afforded to MPAs. However key differences in perceptions were evident from stakeholders both between and within groups, and between stakeholders and MPA managers. A novel legitimacy framework and a stakeholder-vetted suite of indicators for legitimacy are presented and recommended for use by MPA managers in assessing the legitimacy of coastal MPAs, before, during and after MPA designation. The results provide an increased understanding of stakeholders’ perceptions of legitimacy, giving managers key additional information needed to establish effective MPAs in the future.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".