It’s Just Conservation: To What Extent Are Marine Protected Areas in the Irish Sea Equitably Governed and Managed?
Bibliographic record
Abstract
It is not enough to simply designate a protected area. According to the Convention on Biological Diversity’s Aichi Target 11, these sites should be governed and managed effectively and equitably. Equitable (i.e., fair and inclusive) conservation is vital to ensuring effective protection of natural resources while maintaining human well-being. Yet, equity tends to be overlooked in protected area assessments. Three marine protected areas (MPAs) in Great Britain, Northern Ireland, and the Republic of Ireland were selected to assess equitable governance and management in the Irish Sea. This is one of the first studies to assess equity across multiple stakeholder groups in MPAs. The Site-level Assessment for Governance and Equity (SAGE) toolkit, developed by the International Institute for Environment and Development (IIED) to address the gap in equity assessments, was used to evaluate equitable governance and management in these MPAs. Based on the three dimensions of equity (recognition, distribution, and procedure), SAGE contains Likert-scale questions to assess good governance by evaluating how different stakeholder groups perceive their protected area’s management and how included they feel in decision-making. Quantitative data from SAGE is complemented by qualitative data from semi-structured interviews with stakeholders to understand the impact MPA management has on local communities and MPA users. The results of this study reveal a lack of communication between MPA authorities and local stakeholders. They highlight the need for co-management in the form of inclusive partnerships as an alternative to the current top-down governance approach favoed in the United Kingdom and Ireland.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".