A Clinical Didactics Analysis of the Use of Proxemics Forms in the Teaching-Learning Process of Sports and Physical Education Setting: A Case Study in Tunisia
Bibliographic record
Abstract
This paper aims to explore the link between teacher’s use of proxemics and the “link to body” and its impact on teaching act through the analysis of the teaching practice of one Physical Education (PE) teacher named (E). Relying on a clinical didactic methodology based on a “case study” (Terrisse, 2003; Ben Jomaa, Chihi, Sghaier, Mami & Kpazaï, 2017), two types of data were collected. The observation and the video recording of two PE sessions (gymnastics and volleyball) allows to obtain quantitative data in terms of the amount of proxemics types (Hall, 1966) used by the teacher in each teaching session. To collect the qualitative data, different types of interviews (already-there, post-stroke, ante and post session) were conducted with the same teacher in different research temporalities. As a result, the triangulation of these two types of data (Huberman & Miles & De Backer, 1991) shows an obvious correlation between the use of proxemics types and the link to body (Jourdan, 2006). When he gets close, especially in gymnastics setting, he maintains an intimate link to the body through touching and manipulating liberally student’s body parts. When he stays distant, he sometimes shows a narcissistic aspect by showing off his corporal skills in terms of his unconscious “impossible to support”, although he sometimes manifests a distant and repulsive link to body when he faces a paucity of knowledge especially in volleyball. https://doi.org/10.26803/ijlter.17.1.9
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".