Construction collective d'élèves du secondaire autour de la question de l'intimidation: analyse de la dynamique interactionnelle.
Bibliographic record
Abstract
Bullying is a pressing issue that challenges educational and other social practitioners, as many students' schooling process is impeded, often significantly, as regards safety and physical and psychological health. This issue, which is widely discussed in the public sphere and is often debated by adults, also concerns youngsters. Here and elsewhere, measures have been implemented to reduce and prevent bullying in schools. How do high school students interpret the phenomenon of bullying when they are invited to discuss it among themselves in focus groups? What do they say and how do they talk about it? Through the theoretical framework of symbolic interactionism this article sheds light on the meanings that high school students attribute to bullying and on the emerging interactive dynamics that emerged during their discussions. The analysis of the discursive segments manifested a co-construction of meanings stimulated by the interactions students engaged in, and fed by different sources of knowledge that revealed, in a broader sense, students’ relationship to knowledge. In conclusion, we stress the important role of adults for mediating interactions that support the transformation of students’ cognitive capacities and enable them to understand the complexity of the problems with which they must deal.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".