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Record W2346959957 · doi:10.3917/i2d.161.0070

Appropriation de tablettes tactiles par des étudiants de la filière « Bibliothèques et Documentation »

2016· article· fr· W2346959957 on OpenAlexaff
Solenn Dupas, Florence Thiault, Jean-Paul Thomas, Marie-Armelle Ni-Camussi, Bertrand Piechaczyk, Catherine Daniel

Bibliographic record

VenueI2D - Information données & documents · 2016
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsBibliothèque et Archives nationales du Québec
Fundersnot available
KeywordsHumanitiesAppropriationArtDocumentationSociologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

[étude] Dans un contexte de développement des supports numériques nomades, l’équipe pédagogique de la licence professionnelle « Gestion et médiation des ressources documentaires » (Université Rennes 2) a mis en place une enquête qualitative auprès d’étudiants équipés en tablettes tactiles afin d’analyser les modalités et les enjeux de l’appropriation de cet outil en situation de formation. Menée par six enseignants-chercheurs et professionnels de la documentation – Catherine Daniel, Solenn Dupas, Marie-Armelle Ni-Camussi, Bertrand Piechaczyk, Florence Thiault, Jean-Paul Thomas – l’étude porte sur les usages personnels, pédagogiques et professionnels de la tablette. Elle aborde notamment les pratiques de veille et de communication, la lecture, la consultation de vidéo et de musique, la création de contenus et l’utilisation de jeux. L’expérimentation a fait l’objet d’une enquête qualitative. Il s’agit ainsi de voir dans quelle mesure la mise à disposition de tablettes peut contribuer à développer la culture numérique des étudiants.

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)0.007
metaresearch head score (Gemma)0.017
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0070.008
Scholarly communication0.0130.007
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.074
GPT teacher head0.315
Teacher spread0.241 · 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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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