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Record W4387667616 · doi:10.3368/le.100.2.111022-0096r

Tropical Forests Provide Gendered Insurance against Illness

2023· article· en· W4387667616 on OpenAlexafffund
Yoshito Takasaki, Oliver T. Coomes, Christian Abizaid

Bibliographic record

VenueLand Economics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of TorontoMcGill University
FundersJapan Society for the Promotion of ScienceUniversity of TorontoSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsScope (computer science)Amazon rainforestProduct (mathematics)Scale (ratio)FishingNatural resourceBusinessNatural resource economicsForest productGeographySocioeconomicsEconomicsEcologyForest managementBiologyForestry

Abstract

fetched live from OpenAlex

Abstract Tropical forest peoples rely on wild resources to cope with adverse shocks, i.e., natural insurance; however, research has shown its limited scope against health shocks that constrain labor supply responses. This paper examines natural insurance against illness through the lens of gender. We conducted a large-scale household survey in the Peruvian Amazon, where wild resource harvesting is mostly done by males. We find that fishing and nontimber forest product gathering increased against female illness and remittances increased against male illness, suggesting that the scope of natural insurance and risk sharing against illness is shaped by gender in a complementary way.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.000

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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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 designObservational
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

Citations1
Published2023
Admission routes2
Has abstractyes

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