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Record W3086497667

Los ambientes de aprendizajes modernos: un componente pertinente para favorecer los procesos de inclusión en el CDI Fe y Alegría Madre Alberta de la ciudad Santiago de Cali.

2020· article· es· W3086497667 on OpenAlexaboutno aff
Dv. Castro Gilon, Ck. Ruiz Muñoz, Ma M. Fernández Sánchez

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typearticle
Languagees
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceInclusion (mineral)GeographySociologyArtGender studies
DOInot available

Abstract

fetched live from OpenAlex

La educación sin lugar a duda es una de las herramientas dispuestas al ser humano para transformar y cambiar todo lo que se encuentra a su alrededor. Y para que dichas transformaciones se den a cabo, es necesario hacer uso de unos facilitadores en las instituciones educativas que serán los encargados de proponer y llevar a cabo cambios que vayan mucho más allá de simples mejoras y en su paso hagan nuevos caminos para alcanzar las metas en común. Para empezar este recorrido es importante comprender que estos nuevos caminos se forman en propuestas innovadoras que, con el adecuado uso de ellas se tornaran en los ambientes propicios para desarrollar los procesos de inclusión, es así como en el presente artículo de reflexión se analizará cómo los ambientes de aprendizajes modernos son un componente pertinente para favorecer los procesos de inclusión en el CDI Fe y Alegría Madre Alberta de la Ciudad Santiago de Cali. Para ello, se tendrá en cuenta la innovación como una apuesta asertiva a transformar lo existente; la inclusión atendida desde diferentes políticas y programas que velan por garantizar el cumplimiento de unas condiciones y contextos adecuados, que establezcan medidas y herramientas en pro de la calidad y equidad.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.333
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.019
Scholarly communication0.0170.004
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.025
GPT teacher head0.339
Teacher spread0.315 · 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 designNot applicable
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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