MétaCan
Menu
Back to cohort
Record W3137407668 · doi:10.24908/ijesjp.v8i1.14285

Redes de colaboración y Formación para el Fomento de la Ingeniería Comprometida: Reflexiones hacia Futuros Posibles

2021· article· es· W3137407668 on OpenAlexvenueno aff
Carolina Salcedo, Mateo de Jesus Vega-Noguera, Juan David Reina-Rozo

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2021
Typearticle
Languagees
FieldSocial Sciences
TopicHigher Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Este escrito explora desde dos perspectivas el ejercicio de colaboración y las implicaciones de trabajo colectivo en redes de formación de ingeniería. Una primera mirada apunta a las reflexiones teóricas de la evolución de la disciplina y las confluencias de la colaboración y las redes, y una segunda apuesta se encuentra en el análisis de diversas redes en el continente Americano, tal es el caso de las redes ESJP con mayor actuación en Norteamérica, REPOS en Brasil y RECIDS en Colombia desde América Latina. A nivel metodológico se realizó una revisión de literatura y de entrevistas semi estructuradas a siete personas que participan de estas. Mediante diferentes canales de difusión digital fue posible vislumbrar sus inicios e ideales que dieron cabida a la formación y consolidación de estos espacios colaborativos. Ante lo cual, se gesta un símil narrativo de tensiones evidentes en el accionar colectivo como el concepto de desarrollo social y, de las potencialidades y oportunidades que se denotan en el presente y nutren un futuro cercano tal como la justicia social. Finalmente, enmarcados en una realidad agobiante y común a la humanidad se formulan algunas ideas alrededor de las posibilidades de acción en el escenario de pos-pandemia.

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)0.016
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0160.041
Scholarly communication0.0280.027
Open science0.0030.013
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0120.002

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 head0.014
GPT teacher head0.391
Teacher spread0.378 · 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 designTheoretical or conceptual
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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Engineering Social Justice and PeaceSame topicHigher Education and SustainabilityFrench-language works237,207