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Record W7071254596

Sistemas alimentarios territorializados en Costa Rica: 100 iniciativas locales para la alimentación responsable y sostenible

2019· other· es· W7071254596 on OpenAlexaboutno aff

Bibliographic record

VenueInvestigative News in Education (Universidad de Costa Rica) · 2019
Typeother
Languagees
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental crisisEnvironmental policyContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

La investigación conjunta e interdisciplinaria entre la Cátedra de Investigación en Derecho sobre la Diversidad y la Seguridad Alimentarias de la Universidad Laval de Canadá; el Programa de Investigación en Derecho Sanitario del Instituto de Investigaciones de la Facultad de Derecho de la Universidad de Costa Rica; Résolis France5 ; la Chaire Unesco en alimentations du Monde6 de la Universidad de Montpellier y Sciences Po Paris7, permitió la conclusión del presente estudio. Este trabajo se integra a las iniciativas desarrolladas en el marco de la Declaración de Québec, del 2 de octubre de 20158. La problemática de investigación consistió en determinar si en Costa Rica existen initiativas que se integran dentro de sistemas alimentarios territorializados. Para ello se realizó la identificación, selección y análisis comparativo de cien iniciativas que contribuyen a desarrollar sistemas alimentarios territorializados en este país. El documento se estructura en cuatro partes: (A) las consideraciones metodológicas; (B) los hallazgos; (C) las conclusiones y (D) la lista categorizada de las iniciativas según la principal externalidad, ubicación, tipo de acción y externalidades secundarias.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.719
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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