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Record W2904835748 · doi:10.1525/elementa.335

Cuba’s agrifood system in transition, an introduction to the <i>Elementa</i> Special Feature

2018· article· en· W2904835748 on OpenAlexaff
Margarita Fernandez, Erin Nelson, Kim Locke, Fernando Funes-Aguilar

Bibliographic record

VenueElementa Science of the Anthropocene · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSustainabilityFood sovereigntyAgroecologyPolitical scienceFood securityAgricultureSovereigntyFood systemsSociologyEconomic growthGeographyPoliticsLawEconomicsEcology

Abstract

fetched live from OpenAlex

Cuba’s experience in sustainable agriculture and agroecology has been the subject of much international attention, particularly as advocates of agroecology aim to demonstrate the feasibility of implementing alternatives to industrial agriculture on a national scale to support ecological resilience, food security, food sovereignty, and human wellbeing. Such attention has increased since relations between the U.S. and Cuba began to normalize, stimulating speculation as to how this will affect Cuba’s advances in sustainability. The Special Feature Cuba Agrifood Systems in Transition analyses the nuances of agroecological development in Cuba. We emphasized amplifying the voices of locally-based researchers and practitioners by targeting manuscript invitations to Cuban scholars and publishing in both Spanish and English. We outline the process, challenges and goals of this unique endeavor and introduce seven articles, all contributions from Cuba except for one, which is a collaboration between U.S. based and Cuba based scholars. These articles unpack some of the complexities of Cuba’s agrifood system transition and draw on specific information and experiences to discuss successes and challenges of this transition. We thus underline the instructive value of the Cuban experience regarding the path to agrifood system sustainability and hope to spark new collaborative opportunities as scholars and citizens around the world look to develop agrifood systems that will sustain human society long into the future. Please refer to Supplementary Materials, Full text Spanish version of this article, for a full text Spanish version of this article.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

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