L'@lphabétisation et les technologies de l'information à l'aube du nouveau millénaire: nouvelle conception, nouvelles perspectives
Bibliographic record
Abstract
Résumé Ce texte à caractère théorique traite d'abord d'une nouvelle conception de l'@lphabétisation qui intègre les technologies de l'information et de la communication (TIC). Nous présentons ensuite un bref survol de la situation de l'alphabétisation au Québec et au Canada afin de montrer qu'il s'agit bien d'un problème actuel qui sévit, en silence, au coeur même des sociétés dites «développées». Nous abordons ensuite l'analphabétisme informatique, un nouveau problème social. Puis, nous teutons d'illustrer des avantages incontestables inhérents à une intégration pédagogique efficiente des technologies : une motivation et un apprentissage accrus pour les apprenants; des impacts positifs importants sur les apprentissages en lecture, en écriture, en calcul et en utilisation des technologies. This text, of a theoretical nature, first presents a new conception of literacy (called l'@phabetisation) which integrates information and communication technologies (ICT). Then, an overview is given of the context of literacy in Quebec and in Canada, in order to show that illiteracy is indeed a contemporary problem that festers in silence at the heart of so-called developed countries. Next, technological illiteracy is examined as a new social challenge. Following this, the undeniable advantages inherent in the efficient pedagogical integration of technologies are illustrated: increased motivation and learning for the students; significant positive effects on learning in terms of reading, writing, numeracy, and the use of technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".