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Record W2799428467 · doi:10.5716/wp16036.pdf

Agroforestry and Forestry in Sulawesi series: Women’s participation in agroforestry: more benefit or burden? A gendered analysis of Gorontalo Province

2016· report· en· W2799428467 on OpenAlexfundno aff
Elok Mulyoutami, Desi Awalina, Endri Martini, Noviana Khususiyah, Isnurdiyansyah Isnurdiyansyah, Janudianto Janudianto, Duman Wau, Suyanto Suyanto

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock Farming and Management
Canadian institutionsnot available
FundersTokyo Metropolitan UniversityGovernment of Canada
KeywordsAgroforestryGeographyForestryEnvironmental science

Abstract

fetched live from OpenAlex

Women and men have different understandings of and knowledge about the natural resources in their environment. These differing knowledge bases influence their practices in managing and extracting natural resources, leading to different results and impacts. This study assesses the respective roles of women and men in households with agroforestry-based livelihoods in Gorontalo Province, Sulawesi, and seeks to show which gender receives the greatest benefits and which faces the greatest challenges in such partnerships. It also seeks to show how couples adapt and coexist in these households and in the wider community. The research findings provide guidance for designing equitable and effective development programmes that ensure that agroforestry livelihoods create more benefits than burdens for both women and men.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations12
Published2016
Admission routes1
Has abstractyes

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