Gender mainstreaming practices in METIs: Some case studies
Bibliographic record
Abstract
The existing gender imbalance, discrimination and difficulties that female students face in Maritime Education and Training (MET) constitutes a widespread concern. To address this situation, Maritime Education and Training Institutions (METIs) have turned to gender mainstreaming looking for strategies and practices that may bring about possible solutions. This paper reviewed existing literature to identify specific case studies on in-depth gender mainstreaming actions undertaken in different METIs. Seven case studies were selected to illustrate specific interventions and practices to mainstream gender and their possible applicability and transferability across institutions. The results of the analysis show that the interventions presented combine different strategies and approaches, but no standard procedure to mainstream gender. However, most proposals describe some common or overlapping practices like the importance of networking and mentoring programmes, considering female students’ expectations and motivations for enrolling in MET, rethinking recruitment and, most importantly, reviewing the maritime curriculum incorporating more gender-inclusive practices. Additionally, most of the interventions analysed reveal benefits for female students and, frequently, also for all students. Hence, most case studies agree to promote the development of gender strategic plans for METIs. In sum, such practices should be extended and transferred across METIs for fostering more inclusive MET environments.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".