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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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

<h3>Abstract</h3> 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.091
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
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