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Record W4238343809 · doi:10.1344/ridas2017.3.13

APRENDIZAJE SERVICIO COMO HERRAMIENTA METODOLÓGICA EN LA EDUCACIÓN SUPERIOR

2019· article· es· W4238343809 on OpenAlexaff
Gema Santander

Bibliographic record

VenueRIDAS Revista Iberoamericana de Aprendizaje y Servicio · 2019
Typearticle
Languagees
FieldSocial Sciences
TopicHigher Education and Sustainability
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsSubject (documents)Orientation (vector space)Relation (database)SociologyService (business)PedagogyHumanitiesPsychologyMathematics educationComputer scienceLibrary sciencePhilosophyBusinessMathematics

Abstract

fetched live from OpenAlex

The present testimony aims to motivate higher education teachers to perform an analysis of the subjects who are dictating at this time or which have in planning and see if they are feasible to enact them as subjects with service learning methodology. It gives a proposal of steps to follow that can serve as a first orientation in relation to the subject, which if real, will contribute to the demonstration of university social responsibility.

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)0.006
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: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.312
Teacher spread0.304 · 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
GenreMethods

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

Citations2
Published2019
Admission routes1
Has abstractyes

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