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Record W4400099390 · doi:10.1016/j.envsci.2024.103819

Uncertainty and perceived cause-effect help explain differences in adaptation responses between Swidden agriculture and agroforestry smallholders

2024· article· en· W4400099390 on OpenAlexaff
Mar Moure, Carsten Smith‐Hall, Birgit Schmook, Sophie Calmé, Jette Bredahl Jacobsen

Bibliographic record

VenueEnvironmental Science & Policy · 2024
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsUniversité de Sherbrooke
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020HORIZON EUROPE Framework ProgrammeHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsLivelihoodContext (archaeology)AgricultureNatural resource economicsPsychological interventionAdaptation (eye)Climate changeBusinessEnvironmental resource managementAgroforestryEconomicsGeographyPsychologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Swidden smallholders are among the most vulnerable groups to climate change. Many efforts have focused on incentivizing their transition to agroforestry, often with limited results. Such transitions, embedded in complex socio-environmental changes, generate uncertainties, often ignored in the science-policy interface. In this paper, we examine dispersed disciplinary developments in decision-making under uncertainty, apply the insights to a case study, and discuss results in the context of prevalent knowledge production assumptions and incentivized livelihood transitions policies. We use interview data from three communities in the Mexican Maya region to create aggregated mental models of smallholders who adopted agroforestry, and those who continue to practice traditional swidden agriculture. The mental models depict perceived causal connections—including uncertain or delayed—between hazards, causes, consequences and responses. Our results show substantial differences in mental models driven by length of explanatory pathways, attribution of hazards and portfolios of responses, suggesting that agroforesters were more prone to proactive behavior and/or more responsive to outside discourses. Agroforestry is effective in reducing some uncertainties in its bundled approach, but new uncertainties for which smallholders have no prior experience arise. Contrastingly, recurrent themes point to lower self-efficacy in swidden smallholders, which may help explain non-adoption. We caution that not recognizing differences in mental models among potential beneficiaries of incentivized interventions may inadvertently exacerbate inequalities, while unaddressed uncertainties may lead to future disadoption. As a scientific tool, mental model mapping can inform the design of adaptation measures by identifying new knowledge and conflicting rationales, and segmenting strategies for potential (non)adopters.

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)0.004
metaresearch head score (Gemma)0.015
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.002
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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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