La littératie informationnelle au primaire : étude de cas de lecteur·rices internautes de 6<sup>e</sup> année
Bibliographic record
Abstract
Le présent article décrit une étude de cas multiples qui s’est penchée sur l’interaction des compétences de navigation, d’évaluation et d’intégration au sein d’un processus menant à la compréhension d’informations consultées en ligne, et ce, lors de la réalisation d’une tâche de littératie informationnelle par des lecteur·rices internautes de 6 e année. L’étude combine l’entretien direct, la verbalisation de la pensée, l’entretien libre de verbalisation de la compréhension et l’entretien rétrospectif semi-dirigé. Elle permet l’induction d’une mobilisation autorégulée des compétences, en plus de permettre, au départ des convergences et des divergences observées entre les cas, la formulation d’une hypothèse de profils de lecteur·rices internautes.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".