Perception et compréhension du concept de citoyenneté mondiale par des enseignants en formation : une comparaison Kazakhstan-Suisse
Bibliographic record
Abstract
La citoyenneté mondiale est un concept récent, discuté dans de nombreuses instances scientifiques et officielles, mais peu étudié de manière empirique. Notre étude se propose alors de comprendre comment il est reçu par les étudiants au Kazakhstan et en Suisse. Nos données, recueillies par questionnaire en ligne et analysées de différentes manières (statistique descriptive, analyse lexico-métrique, analyse de contenu) en fonction des types de questions, permettent de mettre en évidence les tensions relatives à la visée universaliste du concept, mais aussi les différences d’appréciation notables de la citoyenneté mondiale en fonction des contextes de vie des participants. Cette étude exploratoire met en évidence la complexité de l’adaptation d’un concept international commun à un monde globalisé dans lequel des inégalités subsistent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".