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Record W4399498303 · doi:10.4000/11sg6

Approches sémiotiques et langagières en physique et en mathématiques

2024· article· en· W4399498303 on OpenAlexaff
Karine Bécu‐Robinault, Luis Radford

Bibliographic record

VenueAnnales de didactique et de sciences cognitives · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSemioticsReciprocalMeaning (existential)Embodied cognitionConstruct (python library)GestureMathematics educationAction (physics)EpistemologySemiosisFunction (biology)Ideal (ethics)Cognitive scienceHumanitiesMathematicsPsychologyComputer scienceLinguisticsPhilosophyPhysicsArtificial intelligenceQuantum mechanics

Abstract

fetched live from OpenAlex

In physics and mathematics, there is a growing interest in studying the meanings that teachers and students construct through the mobilization of several semiotic systems, including embodied action, such as gestures, body postures, rhythm and speech. In this article, we propose a comparison of two approaches, developed in parallel in each of these disciplines. In physics, it is the function of models in the scientific community of physicists that guides the consideration of semiotic systems to account for the reciprocal meaning of material situations and taught concepts. In mathematics, it is the very nature of activity - considered at the same time as ideal, material and sensible - which leads to a consideration of the semiotic systems which underlie it, a consideration which allows us to shed new light on the processes of teaching and learning

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)0.011
metaresearch head score (Gemma)0.012
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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.024
Scholarly communication0.0100.014
Open science0.0020.006
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.360
Teacher spread0.322 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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