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Record W4213072114 · doi:10.1111/jiec.13241

Can the Caribbean localize its food system?: Evidence from biomass flow accounting

2022· article· en· W4213072114 on OpenAlexaff
Shupa Rahman, Simron Jit Singh, Cameron McCordic

Bibliographic record

VenueJournal of Industrial Ecology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFood securityContext (archaeology)Caribbean regionBiomass (ecology)GeographyResource (disambiguation)Food systemsFood policyDevelopment economicsAgricultural economicsLatin AmericansEconomicsEcologyPolitical scienceAgricultureBiology

Abstract

fetched live from OpenAlex

Abstract Small island economies are highly dependent on food imports. Self‐sufficiency through food localization is therefore often advocated. Can a small Caribbean island nation localize its food system? To answer this question, we conducted socio‐metabolic research on four Caribbean nations: Barbados, Dominica, Grenada, and Jamaica. Derived indicators from a diachronic biomass flow accounting from 1961 to 2019 suggest a declining trend in local food production for all cases. While in Barbados and Jamaica this decline already began in the 1960s, for Dominica and Grenada, this did not start until the late 1970s–1980s. The physical trade balance of biomass is similar across all cases: from net exporters at the start of the study period to net importers as countries developed, albeit at different time periods. By disaggregating biomass flow data to crop level, Barbados and Jamaica indicate a trend that is moving away from food localization, while Dominica and Grenada appear to be modestly moving toward localization in recent years. Given the many resource security challenges small island economies face, this study provides a biophysical perspective to the Caribbean's food security debate and questions the extent to which food localization is possible in a small island context, and whether other strategies are urgently needed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.210
Teacher spread0.168 · 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 designNot applicable
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

Citations17
Published2022
Admission routes1
Has abstractyes

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