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Record W3138759622 · doi:10.24908/ijesjp.v8i1.14690

Fluir como el agua en el trabajo con comunidad: Entrevista a Mariela García Vargas

2021· article· es· W3138759622 on OpenAlexvenueno aff
Mariela García Vargas, Nicolás Gaitán-Albarracín, Angie Serna, Maria Elisa Palacios Possu

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2021
Typearticle
Languagees
FieldEnvironmental Science
TopicPublic Health and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsGarciaErasmus+HumanitiesPolitical scienceGeographySociologyArtArt history

Abstract

fetched live from OpenAlex

Mariela García Vargas es socióloga egresada de la Universidad Nacional de Colombia, con Maestría en estudios del Desarrollo, del International Institute of Social Studies (Erasmus University Rotterdam). Actualmente es profesora asociada en la Universidad del Valle, está adscrita al Instituto CINARA de dicha Universidad y conjuntamente con sus colegas del Instituto ha trabajado en proyectos de gestión comunitaria de agua. Durante su trayecto académico y laboral, Mariela se ha destacado por implementar el enfoque de género, la investigación participativa y resaltar la importancia del estudio sociocultural en distintos proyectos de ingeniería en Latinoamérica.

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)0.008
metaresearch head score (Gemma)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.285
Teacher spread0.277 · 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
Published2021
Admission routes1
Has abstractyes

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