Advancing gender equality in international ocean science: Participatory approaches for institutional actions
Bibliographic record
Abstract
Gender inequality in international marine and ocean science is an ongoing barrier to ocean governance sustainability goals and requires systemic institutional transformation. Examining how participatory approaches can be applied at international level and contribute to advancing a gender equality agenda can help identify pathways for change. This study uses a timeline of a feminist participatory action research (FPAR) to explore its application within the International Council of the Exploration of the Sea (ICES), working with its community of marine science professionals, to advance gender equality. By documenting and mapping formal and informal actions against a theory of change, the research reveals that stages of awareness, understanding, and action are dynamic and overlap. Despite varying degrees of short-term acceptance and resistance, ongoing community engagement, monitoring, and evaluation is essential for fostering longer-term change. The analysis demonstrates how FPAR can be applied in an intergovernmental organization, with multiple factors, including drivers, data collection, co-creation through dialogue, and specific actions contributing to driving a change agenda and the formulation and publication of a gender equality plan for the organization. The results demonstrate that meaningful actions can occur at various scales, with participatory engagement as central. The results provide evidence of how gender equality as a global meta-norm is diffused into practice and are discussed in the context of the limitations of formal gender equality plans to realize change.
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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 | 0.141 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.071 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| 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".