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Record W2759150808

Food Security in Cuba: A Comparison of TRDs, Bodegas, and Agricultural Markets

2015· article· en· W2759150808 on OpenAlexaffvenue
Rebecca Stockton

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCuban History and Society
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFood securityRationingAgricultureBusinessFood systemsTourismCurrencyAgricultural economicsEconomicsEconomic growthGeographyBiologyMonetary economicsEcology
DOInot available

Abstract

fetched live from OpenAlex

After the collapse of the Soviet Union, Cuba entered a period of economic hardship known as the Special Period in Times of Peace.  Since then, changes in Cuba’s food policy have reflected a shift away from strictly regulated food rationing to a mixed system including the libreta system or rationing as well as agricultural markets and stores using convertible pesos (TRDs).  This system has lead to overall greater food security but also greater disparities in food accessibility.  This study reviews and analyses food prices in bodegas, agricultural markets, and TRDs in several municipalities in Cuba. Most of the food items found in the TRDs were unavailable in bodegas and agricultural markets.  This confirmed a trend in which more food items are becoming available only in TRDs.  In addition, TRDs only accepted converted pesos (CUCs) as opposed to national pesos (CUPs), creating a disparity between those with access to the tourism industry and those who do not.  While food availability is greater than it was in the early 1990’s, there are still issues surrounding food access for those without access to CUCs, linking food security to the tourism industry in Cuba.  However, policy changes regarding currency unification and changes regarding the libreta system will impact food security issues in the future.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.410
Teacher spread0.284 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes2
Has abstractyes

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