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Record W2114559562 · doi:10.3390/su1041366

Negotiating Uncertainty: Jamaican Small Farmers’ Adaptation and Coping Strategies, Before and After Hurricanes—A Case Study of Hurricane Dean

2009· article· en· W2114559562 on OpenAlexaff
Donovan Campbell, Clinton L. Beckford

Bibliographic record

VenueSustainability · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsLivelihoodNegotiationAgricultureVulnerability (computing)Coping (psychology)GeographySocioeconomicsStressorClimate changeEnvironmental resource managementEnvironmental planningSociologyEnvironmental sciencePsychologyOceanographySocial science

Abstract

fetched live from OpenAlex

In recent years, Jamaica has been seriously affected by a number of extreme meteorological events. The one discussed here, Hurricane Dean, passed along the south coast of the island in August 2007, damaging crops and disrupting livelihood activities for many small-scale farmers. This study is based on detailed ethnographic research in the southern coastal region of St. Elizabeth parish during the passage of Hurricane Dean, and explores the ways in which small farmers negotiate the stressors associated with hurricane events. The study employed a mix methods approach based on a survey of 282 farming households. The paper documents coping strategies employed by farmers in the immediate period of Hurricane Dean to reduce damage to their farming systems, and highlights the positive correlation between farmers’ perceptions of hurricanes and degree of damage to local farming systems. In addition, through an analysis of socio-economic and environmental data, the paper provides an understanding of the determinants of adaptive capacity and strategy among farmers in the area. The study indicated that despite high levels of vulnerability, farmers have achieved successful coping and adaptation at the farm level.

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 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 score0.355
Threshold uncertainty score0.992

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 head0.024
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 teacher head, 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

Citations40
Published2009
Admission routes1
Has abstractyes

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