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Record W2789250754 · doi:10.26803/ijlter.17.2.6

Teaching Practice between Ostension and Proximity: The Case of a Seasoned Physical Education Teacher in Clinical Didactics

2018· article· en· W2789250754 on OpenAlexaff
Hiba Abdelkafi Karoui, Hejer Ben Jomâa, Georges Kpazaï

Bibliographic record

VenueInternational Journal of Learning Teaching and Educational Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsProxemicsPhysical educationProcess (computing)Qualitative researchPsychologyClinical PracticeMathematics educationPedagogySociologyComputer scienceMedicineCommunicationNursingSocial science

Abstract

fetched live from OpenAlex

This study aims, within a clinical didactics framework, to identify the crucial role of “proxemics” (Hall, 1966, 1973), as a major driver of the non-verbal interaction within the teaching-learning process in Physical Education (PE) and the relationship that it maintains with ostension. We proceeded with a double analysis (quantitative and qualitative) case study of a Physical Education teacher’s in situ practice in order to identify this singularity (Terrisse, 2000). Results show a significant dependence between the use of didactic distance types (Forest, 2006) and ostension types (Salin, 2002) utilized in his teaching practice. As a result, we noticed that “distance” is intimately related to different types of ostension and has a major role, as an implicit form of interaction, in the regulation and the management of didactic situations. https://doi.org/10.26803/ijlter.17.2.6

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)0.003
metaresearch head score (Gemma)0.010
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 score0.019
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.014
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.146
GPT teacher head0.567
Teacher spread0.421 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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