An inequity assessment framework for planning coastal and marine conservation and development interventions
Bibliographic record
Abstract
Sustainable development should promote equity with benefits for coastal communities. Many conservation and development initiatives promise to contribute to an equitable future without being designed to do so. Here, we promote an assessment tool to help interventions plan to promote equity through forecasting and evaluating the risks of contributing to inequities, in order to plan against them. Building from rich literatures of impact assessment, procedural justice, postcolonial studies, critical race theory, and fields in sociology studying the accrual of advantage and disadvantage among different groups, we propose the assessment framework follow key principles that center on understanding how interventions affect marginalized people, and assess how planning, implementation, and outcome decisions build on each other and reflect (or work against) broader systemic contextual pressures that perpetuate inequities. In forecasting and monitoring potential inequities, coastal communities and proponents of interventions should be able to plan against the realization of these adverse impacts. We show how the framework can be used in three case studies: 1) a climate adaptation project; 2) marine protected areas; 3) a debt relief program. Sustainable development is about promoting equity, but only with methods employed to confront and understand inequitable consequences can interventions do so.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".