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Record W4386157427 · doi:10.48160/22504001er27.474

Semillas sin cosecha: Gestión del conocimiento agropecuario y transformación del paisaje en la Sabana de Bogotá, Colombia (1907-1990)

2023· article· es· W4386157427 on OpenAlexaff
Omar Ruiz-Nieto, Martín Giraldo-Hoyos

Bibliographic record

VenueEstudios Rurales · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicHistory and Politics in Latin America
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesGeographyPolitical scienceArt

Abstract

fetched live from OpenAlex

Durante el siglo XX, la Sabana de Bogotá fue uno de los espacios escogidos por el Estado colombiano para diseñar y poner en práctica las instituciones científicas que acarrearon la modernización de la agricultura de vocación altoandina. Las estrategias utilizadas para la gestión de los conocimientos agropecuarios importados y producidos localmente por estas instituciones se plantearon el incremento de la producción de alimentos para mejorar la calidad de vida de los colombianos. Sin embargo, estos programas de investigación y extensión terminaron por impactar los sistemas agroalimentarios locales y dieron paso a la entrada de la agroindustria de flores ornamentales. Este artículo estudia las contradicciones entre las estrategias utilizadas por el Estado colombiano para la gestión del conocimiento agropecuario y los impactos socioecológicos de estos programas en los paisajes locales. Para lo cual se hace un recorrido por la historia de las instituciones científicas agropecuarias en la Sabana de Bogotá y se evidencia su injerencia en las transformaciones del paisaje local entre 1926 y 1990.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.319
Teacher spread0.299 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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