Le chanoyu, cérémonie japonaise traditionnelle du thé, comme exemple d'éducation à la présence
Bibliographic record
Abstract
Que peut en retour, et par le détour, nous apprendre de manière distanciatrice le chanoyu de nos manières d’apprendre en Occident, et de nous construire dans l’interaction ? A l’heure de l'essor fulgurant des TICE, cette communication se propose de revenir sur les fondements d’une cérémonie traditionnelle du thé japonaise, d’envisager ses principes esthétiques et son « intentionnalité », avant d’interroger le concept singulier de technique mis en œuvre. L’objectif visé étant de souligner l’importance plus que jamais cruciale de l’ouverture du « chantier » d’une véritable « éducation à la présence » comme vecteur essentiel d’une éducation interculturelle électronique et présentielle adaptée aux défis du XXIe siècle. Chanoyu, the traditional Japanese tea ceremony as an example of in-presence education What can be learnt when taking a reflexive distance from the chanoyu concerning the ways in which we in the West learn and construct ourselves in interactions? With the meteoric rise of Information and Communication Technologies (ICT), this paper seeks to reexamine the basis of the traditional Japanese tea ceremony with its aesthetic principles, its “intentionality” and the singular concept of technique it puts into practice. The objective of this approach is to highlight the ever-important opening of a research domain into a veritable “in-presence education” as an essential vector in intercultural and in-presence electronic education adapted to the challenges of the 21st century.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".