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Record W1904792597 · doi:10.24908/pceea.v0i0.5730

ELECTRIZARTE: COMBINING ENGINEERING EDUCATION, ARTS AND SOCIAL OUTREACH IN AN EXTRA-CURRICULAR ACTIVITY

2015· article· en· W1904792597 on OpenAlexvenueno aff
Teodoro Willink, Gustavo Núñez, Lochi Yu

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicScience, Technology, and Education in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachCreativityLimitingTheme (computing)Engineering educationThe artsEngineeringEngineering ethicsVocational educationProject-based learningMathematics educationComputer scienceEngineering managementPedagogySociologyPsychologyMechanical engineeringVisual artsPolitical science

Abstract

fetched live from OpenAlex

In the Electrical Engineering undergraduate program of the University of Costa Rica, most of the design and analysis projects are guided, structured and related to a specific topic, limiting technical content and machining the way problems are solved, which in turn, restricts the motivated student creativity. The ElectrizArte Project provides the opportunity to develop both technical and not-technical skills in a extra-curricular enviroment. As the main theme, ElectrizArte allows students to design, implement, and operate projects that combine engineering with art, taking advantage of the particular situation that many of the students participating in the project, have interest in art in general, or practice some artistic activity.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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
Published2015
Admission routes1
Has abstractyes

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