Social equity indicators for a Blue Economy: Guidelines for application
Bibliographic record
Abstract
Social equity goals are specifically stated in most new ocean management and development plans, notably those aligned with a Blue Economy that recognizes the significant and rising inequities across ocean spaces and sectors. However, the deeper significance of social equity has still not been fully integrated in policy and management practices, which often ask why it is relevant and how it can be measured in ways comparable to more familiar ecological and economic indicators. We propose a theoretical and practical framework (including examples of indicators) that builds on key concepts in critical environmental justice, antiracist and decolonial scholarships: difference (not all groups have equal opportunity, so interventions must serve the least well-off); intersectionality (people and groups have multiple self-identities); power relations (colonization has entrenched an ‘us-vs-them’ mentality); and interest convergence (goals of marginalized groups are often only pursued if they benefit the powerful). These principles are contrasted with three categories of indicators that sequentially build toward evaluating social equity goals: total outputs (what and how much do ocean sectors contribute or impact); disaggregated impacts (who shares in specific benefits and costs); and equity actions (specific actions taken to implement equitable processes and outcomes). Local, national, and intergovernmental evaluation plans can use these guidelines to clarify what aspects of development are being reflected in indicators, and what indicators need to be added for a fuller picture of outcomes that support the interests of marginalized populations across the world’s oceans.
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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 | 0.113 | 0.251 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.034 | 0.023 |
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".