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Record W2914308533 · doi:10.24908/ijesjp.v6i1.12675

Comunidades de Aprendizaje como apoyo a un Sistema de Gestión del Conocimiento y la Innovación. Un estudio de Caso

2019· article· es· W2914308533 on OpenAlexvenueno aff
Ángel Alonso Pérez, Ernesto Lleras, Julia Díaz

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2019
Typearticle
Languagees
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En el este artículo presentamos un estudio de caso donde se usa el enfoque de Comunidades de Aprendizaje (CA) para propiciar un espacio de innovación dentro de una organización. En el caso se busca fortalecer el Sistema de Gestión del Conocimiento y la Innovación (SGCI) de una empresa de servicios públicos, estableciendo relaciones de aprendizaje entre los trabajadores. Es estas relaciones de aprendizaje las personas asumen roles diferenciados (de mentores o aprendices) y pueden desarrollar capacidades que les permiten proponer nuevas prácticas a la empresa.

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)0.007
metaresearch head score (Gemma)0.015
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0100.006
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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