Perspectives on Conceptual Change
Bibliographic record
Abstract
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)?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.067 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".