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Record W4411061674 · doi:10.3390/land14061210

Indigenous Knowledge, Gender and Agriculture: A Scoping Review of Gendered Roles for Food Sustainability in Tonga, Samoa, Solomon Islands and Fiji

2025· review· en· W4411061674 on OpenAlexfundno aff
Nidhi Wali, Nichole Georgeou, Seeseei Molimau-Samasoni

Bibliographic record

VenueLand · 2025
Typereview
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersAustralian Centre for International Agricultural ResearchInternational Development Research Centre
KeywordsIndigenousSustainabilityAgricultureGeographyTraditional knowledgeSocioeconomicsEnvironmental planningEconomic growthEcologySociologyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.326
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.409
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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