Bibliographic record
Abstract
The view that argumentation is a desired reasoning practice in the classroom is well reported in the literature. Nonetheless, it is still not clear what type of reasoning supports classroom argumentation. The paper discusses abductive reasoning as the most adequate for students’ arguments to emerge in a classroom discussion. Abductive reasoning embraces the idea of plausibility and defeasibility of both the premises and the conclusion. As such, teachers’ role becomes the one of guiding students through formulating relevant hypotheses and selecting the most plausible one according to criteria. Argumentation schemes are proposed as useful tools in this process.L'idée que l'argumentation est une pratique de raisonnement souhaitée en classe est bien documentée dans la littérature. Néanmoins, il n'est toujours pas clair quel type de raisonnement soutient l'argumentation en classe. Dans cet article on discute du raisonnement abductif comme étant le plus adéquat pour que les arguments des élèves émergent dans une discussion en classe. Le raisonnement abductif emploie l'idée de plausibilité et de la révocabilité des prémisses et de la conclusion. En tant que tel, le rôle des enseignants consiste à guider les élèves à formuler des hypothèses pertinentes et à sélectionner le plus plausible selon des critères. Les schèmes d'argumentation sont proposés comme des outils utiles dans ce processus.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".