Indigenous Knowledge, Gender and Agriculture: A Scoping Review of Gendered Roles for Food Sustainability in Tonga, Samoa, Solomon Islands and Fiji
Bibliographic record
Abstract
This scoping review examines the state of academic knowledge around gender and its role in Indigenous/traditional knowledge for food sustainability in Tonga, Samoa, Solomon Islands and Fiji. The different roles played by all genders—men, women and non-binary—in the Pacific Islands can contribute to climate adaptation and knowledge preservation for sustainable food production. The gender lens is especially relevant given the fact that women’s knowledge has, in recent years, been disregarded and marginalised as a consequence of colonial influences and increasing reliance on imported foods. We analysed 14 studies published in English between 2015 and 2024—six from refereed journal articles and eight from grey literature. Three themes emerged linking agriculture, gender and traditional knowledge, as follows: (1) there is a gendered division of labour and culturally defined roles between women and men, although the roles played by non-binary groups remain unclear; (2) intergenerational traditional knowledge transmission has declined; (3) climate change adaptation could be reinforced through passing on traditional knowledge. The findings demonstrate that gendered knowledge is distinct and complementary, and this knowledge should be integrated into Pacific agricultural production to achieve resilient and sustainable farming in the face of climate 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".