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Record W4235064418 · doi:10.24908/ijesjp.v8i1.14275

Mappa Ingeniare

2021· article· es· W4235064418 on OpenAlexvenueno aff
Laura Upegui, Andrés Acero

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2021
Typearticle
Languagees
FieldComputer Science
TopicEducational Technology in Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPanoramaPhilosophyPolitical scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Un mapa interactivo que presenta un panorama de posibilidades para una práctica comprometida y abierta de la ingeniería. El mapa cuatro secciones, cada una con un video correspondiente en YouTube, que se pueden recorrer en cualquier orden. Todas las ideas presentadas son subjetivas, los autores invitan a continuar la discusión en la herramienta de “comentarios” en los enlaces de dichos videos.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.127
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.006
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1270.067

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 head0.012
GPT teacher head0.289
Teacher spread0.278 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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