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Notice bibliographique
Résumé
Scientific Explanation, Systematicity, and Conceptual Change Organizer and Chair: David R. Kaufman Cognition and Development, Graduate School of Education University of California, Berkeley; Berkeley, CA, 94720 email: davek@socrates.berkeley.edu Speakers: Stella Vosniadou Department of History and Philosophy of Science National and Capodistrian University of Athens; Athens, Greece email: svosniad@athena.compulink.gr Andy diSessa Cognition and Development, Graduate School of Education University of California, Berkeley; Berkeley, CA, 94720 email: disessa@soe.berkeley.edu Paul Thagard Philosophy Department University of Waterloo: Waterloo, Ontario, N2L 3G1 email: pthagard@watarts.uwaterloo.ca Introduction Humans possess remarkably rich and adaptive conceptual knowledge systems that enable them to form relatively stable representations about the world, perceive coherence amidst noise and chaos, and communicate elaborate explanations to others who see the world in strikingly similar ways. On the other hand, knowledge can sometimes be surprisingly brittle and context-bound, coherence may be more illusory than real, and individuals (e.g., teachers and students) may repeatedly fail to achieve common ground during routine discourse. How can we account for such apparent contradictions? Conceptual change names a family of theories, methodological approaches, and research traditions concerned with the origin, ontogenesis, and evolution of knowledge systems as a result of formal and informal learning. Conceptual change is the subject of considerable research across all of the cognitive sciences. In particular, it is central to investigations in the philosophy of science, cognitive development, and science education. The speakers in this symposium will address issues in conceptual changes as they pertain to children, students learning science, lay adults, and practicing scientists. They will consider philosophical, developmental, computational, and instructional issues related to the characterization of systematicity and coherence in scientific explanation. The participants will offer distinct and sometimes divergent points of view on conceptual change with particular attention to the reasons and mechanisms that produce systematicity and coherence (and alternatively incoherence) within and across individuals in generating scientific explanations. The speakers will address a range of related questions, including the following: How can we characterize the state of knowledge structures prior to formal learning? What happens to students’ knowledge when it makes contact with formal learning? What are the knowledge elements that undergo change in conceptual change (e.g., beliefs, theories, schemata, propositions, and coordination classes)? What constitutes evidence for such changes? What are “common” or “typical” trajectories in conceptual development (e.g., from atheoretical to theoretical, incoherent to increasingly coherent)? How can we account for periods of stability and instability in the generation of scientific explanations? What are the mechanisms of change (e.g., differentiation, belief revision, enrichment, conceptual combination, re-organization and reprioritization of knowledge elements)?
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,004 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,039 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle