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Record W4320912363 · doi:10.48090/ciki.v1i1.1272

EVALUACIÓN DE ESTRATEGIAS DE APRENDIZAJE CON TRANSFERENCIA DIGITAL E INNOVACIÓN EMPRESARIAL PARA LA EFECTIVA DIFUSIÓN DE LOS OBJETIVOS DE DESARROLLO SOSTENIBLE EN META COLOMBIA CON AYUDA DE UN MODELO EMPRESARIAL SOSTENIBLE

2023· article· es· W4320912363 on OpenAlexaff
Mauricio Eduardo Cabra Lopez, Alben Cardenas, Pedro Gómez, Hector Murcia, Cristina Guzmán

Bibliographic record

VenueAnais ... Congresso Internacional do Conhecimento e Inovação · 2023
Typearticle
Languagees
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsUniversité du Québec à Trois-RivièresMagna International (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

La problemática identificada es el deficiente conocimiento sobre los Objetivos de Desarrollo Sostenible de la agenda 2030 en el área de Mesetas y Lejanías, en Meta, Colombia. La problemática se resolvió, compartiendo a nivel local, una estrategia de aprendizaje con transferencia digital e innovación empresarial con la participación de estudiantes de grado 10 y 11 y productores rurales de las localidades de estudio de utilidad para la difusión de los ODS en el sector rural del Ariari en Colombia. Se utiliza como modelo un negocio exitoso sostenible y se obtuvieron resultados inherentes a evaluación de efectividad de técnicas para divulgación de conocimientos, actitudes y prácticas, así como implantación de estrategias metacognitivas de utilidad para la difusión de los ODS en Meta. Los datos obtenidos en la investigación implican estrategias de apoyo efectivas para autoevaluación y la práctica para consolidación del aprendizaje sobre los ODS.

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)0.009
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.328
Teacher spread0.289 · 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
Published2023
Admission routes1
Has abstractyes

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