Advancing Social Equity in and Through Marine Conservation
Bibliographic record
Abstract
Substantial efforts and investments are being made to increase the scale and improve the effectiveness of marine conservation globally. Though it is mandated by international law and central to conservation policy, less attention has been given to how to operationalize social equity in and through the pursuit of marine conservation. In this article, we aim to bring greater attention to this topic through reviewing how social equity can be better integrated in marine conservation policy and practice. Advancing social equity in marine conservation requires directing attention to: recognition through acknowledgment and respect for diverse peoples and perspectives; fair distribution of impacts through maximizing benefits and minimizing burdens; procedures through fostering participation in decision-making and good governance; management through championing and supporting local involvement and leadership; the environment through ensuring the efficacy of conservation actions and adequacy of management to ensure benefits to nature and people; and the structural barriers to and institutional roots of inequity in conservation. We then discuss the role of various conservation organizations in advancing social equity in marine conservation and identify the capacities these organizations need to build. We urge the marine conservation community, including governments, non-governmental organizations and donors, to commit to the pursuit of socially equitable conservation.
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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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| 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".