Guest Editorial: Gender in Aquaculture and Fisheries - Moving the Agenda Forward
Bibliographic record
Abstract
1. 17 Agnew Street, Aspley, Queensland, 4034. Australia. 2. Department of Sociology, Memorial University, St. John’s, Canada NL A1C 2Z1. 3. 147 Cangkat Delima Satu, Island Glades, 11700 Penang, Malaysia. 4. Gender and Development Studies, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand. 5. Marine Laboratory, University of Guam, Mangilao GU 96923 USA. 6. Central Institute of Fisheries Technology, CIFT Jn., Matsyapuri P.O., Cochin – 682 029, Kerala, India. 7. Department of Fisheries and Aquaculture, Food and Agriculture Organization, Viale delle Terme di Caracalla, 00153 Rome, Italy.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".