MétaCan
Menu
Back to cohort
Record W2105011836 · doi:10.1017/s1355770x03001232

Risk coping strategies in tropical forests: floods, illnesses, and resource extraction

2004· article· en· W2105011836 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.

Bibliographic record

VenueEnvironment and Development Economics · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlood mythCoping (psychology)FishingMicroinsuranceLivelihoodBusinessNatural resource economicsBushmeatEnvironmental resource managementEconomicsGeographyAgricultureRisk managementFisheryEcology

Abstract

fetched live from OpenAlex

This paper examines coping strategies in response to covariate flood shocks and idiosyncratic health shocks among riverine peasant households in the Amazonian tropical forests. An assessment of coping strategies reveals that although precautionary savings (food stock and livestock) are important for both types of shocks, ex post labor supply responses in the form of upland cropping and resource extraction (fishing and non-timber forest product gathering) are more common to cope with the flood shock depending on local environments. A bivariate probit model examines what factors shape households' adoption decisions of gathering and fishing as a coping strategy. The analysis reveals an important insurance role of non-timber forest product gathering for the asset poor who have limited options for coping with flood risk. Targeted interventions and programs for the poor to promote sustainable forest resource use are discussed.

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 score0.167
Threshold uncertainty score0.189

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 head0.008
GPT teacher head0.177
Teacher spread0.170 · 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