Assessing and improving social equity in marine conservation: background, methods and guidance on three approaches
Bibliographic record
Abstract
Social equity is increasingly recognised as a fundamental principle in marine conservation. Global conservation policies now contain commitments to equitable management and governance, yet practical guidance on how to understand and assess equity in marine conservation remains limited. In this methodological paper, we introduce our process for developing three conceptually grounded, practical and adaptable approaches for assessing equity in marine conservation: (1) a rapid equity assessment, (2) a stakeholders and rightsholders equity assessment, and (3) a co-produced and customised equity assessment. All three approaches emphasise the assessment process as part of an ongoing learning journey that requires continuous reflection and adaptive actions to improve social equity. The discussion identifies practical lessons and key considerations for choosing, preparing and carrying out equity assessments and for moving from assessment to action to improve social equity in marine 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 | 0.074 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".