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El telèfon mòbil: Dimensió i recurs educatiu. Treballar amb imatges en el context de les arts visuals.

2020· article· en· W3043258380 on OpenAlexaff
David Mascarell Palau

Bibliographic record

VenueTemps d'educació/Temps d'educació · 2020
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsExtendicare (Canada)
Fundersnot available
KeywordsContext (archaeology)Mobile phoneRelevance (law)Perspective (graphical)SociologyDimension (graph theory)The artsVisual artsPhonePedagogyHumanitiesArtMultimediaComputer sciencePolitical scienceHistoryLinguistics

Abstract

fetched live from OpenAlex

This text is part of the doctoral thesis ICT in the university teacher education. The mobile phone in the Teaching of Artistic Expression at the Faculty of Pedagogy at the Universitat de València (2017a). It offers a many-faceted view of the mobile phone – its dimension, evolution, social and historical function – and explores the opportunities that this device brings in an educational and artistic context through Mobile Learning. From the perspective of the Visual Arts, our interest lies in working through images and offering alternative work proposals. The theory related to Mobile Learning is presented, underlining the benefits that mobile and ubiquitous learning can represent today. The teaching opportunities that we must promote in the classrooms of the 21st century are assessed in terms of their relevance and appropriateness in the social and technological reality that surrounds us.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0310.005

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 head0.024
GPT teacher head0.314
Teacher spread0.290 · 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 designQualitative
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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