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Record W4318829805 · doi:10.31948/biumar6-1-art15

La investigación de impacto social desde el programa de Mercadeo

2022· article· es· W4318829805 on OpenAlexaff
Lorena Tatiana Luna Tobar, Loreyn Gabriela Erazo Rodríguez, Edgar Mauricio Salas Leiva

Bibliographic record

VenueRevista BIUMAR · 2022
Typearticle
Languagees
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

La discapacidad visual es un tema complejo que genera grandes consecuencias sociales y económicas y conlleva graves efectos psicosociales, especialmente si se presenta en niños, afectando su calidad de vida y la de sus familias. Los niños ciegos tienen grandes necesidades en su diario vivir, dentro del cual se encuentra la educación, que es un resultado directo de la capacidad limitada para apreciar el ambiente que les rodea de forma adecuada y, como consecuencia, emplean otros sentidos para explorar, obtener información y aprender. En este sentido, su educación debe incorporar componentes y elementos específicos que puedan responder a sus necesidades específicas. De esta forma, nació la idea de crear un prototipo de material didáctico que vaya en consonancia con estas carencias.

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)0.003
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.027
GPT teacher head0.338
Teacher spread0.311 · 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
Published2022
Admission routes1
Has abstractyes

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