Introduction to Gender, OIN Project, and Ocean Literacy
Bibliographic record
Abstract
Abstract This chapter highlights the key role of the gender dimension in enhancing ocean literacy, focusing on the necessity to close the gender gap in education, research, and policymaking in the realm of ocean protection. It centres around the Ocean Incubator Network (OIN) project, which exemplifies how integrating gender considerations can significantly influence research and education outcomes as well as teamwork dynamics. After an analysis of the gendered impacts of climate change and the prevalent gender disparities in STEM and research sectors, the chapter underscores the challenges faced by women and the imperative for gender equality to foster effective and inclusive environmental policies. The core discussion then moves to the analysis of the OIN Project’s adoption of a gender-focused approach, emphasizing feminist research methodologies that empower women and girls to share their unique insights and experiences. This strategy not only enhances research and education but also cultivates inclusive spaces and paves the way for visionary leadership models that redefine and shape the future of ocean literacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.009 |
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".