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Record W2064253394 · doi:10.1002/jid.1564

The effect of environmental change and price policies on livelihoods in tropical agroforestry systems

2009· article· en· W2064253394 on OpenAlexaff
Unai Pascual, Roberto Martı́nez-Espiñeira

Bibliographic record

VenueJournal of International Development · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsLivelihoodEndogeneityEconomicsReforestationVirtuous circle and vicious circleNatural resource economicsPovertyTropicsShifting cultivationLand degradationAgroforestryAgricultural economicsAgricultureGeographyEconomic growthEcologyEnvironmental scienceMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Shifting cultivation is one of the most widely distributed forms of agroforestry in the tropics. This paper assesses the potential of using price policies prompting labour mobility to break the fallow crisis typical of such systems leading to the well‐known vicious circle of land degradation and increased poverty. Given changing environmental conditions and endogeneity of household choices, a numerical bio‐economic model is used, based on data from Mexico, to simulate possible scenarios. Results suggest that reducing staple prices can achieve a win‐win outcome when farmers are not constrained to exit and enter the shifting cultivation system. This provides support for the ‘spontaneous reforestation’ hypothesis due to out‐migration when the correct economic policies are in place. Copyright © 2009 John Wiley & Sons, Ltd.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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