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Record W2704179348 · doi:10.1344/ridas2017.3.7

LA PRODUCCIÓN CIENTÍFICA EN TESIS DOCTORALES SOBRE APRENDIZAJE SERVICIO EN ESPAÑA (2000-2016)

2019· article· es· W2704179348 on OpenAlexaff
R García

Bibliographic record

VenueRIDAS Revista Iberoamericana de Aprendizaje y Servicio · 2019
Typearticle
Languagees
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsChristian ministryContext (archaeology)Thematic mapLibrary scienceService (business)ProductivitySociologyGeographyPedagogyPolitical scienceCartographyComputer scienceBusinessMarketing

Abstract

fetched live from OpenAlex

The interest in knowing the trends of research conducted at the university level on service learning in the last two decades in the Spanish context has led to inquire the Doctoral Thesis on this topic in the period between 2000 and 2016. From the data collected into the TESEO database from the Ministry of Education, Culture and Sport it has been done a content analysis of the titles, descriptors and abstracts, assessing the productivity of the area, the main general and methodological characteristics and the thematic studied. The study has shown increasing interest in the academic and scientific field onto service learning as it is a teaching methodology that promotes the development of various competencies through educational activities including community service. The university context stands as one of the priority for the development of SL programs.

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)0.029
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0240.023
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.010
GPT teacher head0.281
Teacher spread0.272 · 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.

Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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