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Record W2950782547 · doi:10.24908/ijesjp.v7i2.13724

Abrir la ciencia para cambiar el mundo

2020· article· es· W2950782547 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2020
Typearticle
Languagees
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La noción de open science no sólo tiene que conjugar todas las formas de dar acceso (papers, data y notebooks), sino también las de promover participación, ya sea incentivando la colaboración entre una heterogeneidad de actores (science shops, citizen panels, consensus conferences, participatory action-research, living labs, hackerspaces, laboratorios ciudadanos, design assemblies) ya sea expandiendo el diálogo de saberes y haciendo más porosas las fronteras entre la academia y la urbe, los expertos y los amateurs, el conocimiento de laboratorio y el de campo, el aula y la plaza o el experimental y el experiencial. Abrir la ciencia también involucra el diseño de infraestructuras que garanticen la soberanía de la comunidad científica, como también abrir el ecosistema o, en otros términos, problematizar los protocolos que regulan la evaluación, financiación y licencia de la investigación, como también la gobernanza de la vida académica, incluidas las convocatorias y los jurados, o los premios, concursos, comisiones y, desde luego, los dispositivos de planeación.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.783
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.282
Teacher spread0.264 · 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