Stability and Change in Science Education -- Meeting Basic Learning Needs: Homeostasis and Novelty in Teaching and Learning
Bibliographic record
Abstract
In this book the editors consider the resistance to change among teachers and learners despite all the evidence that science participation brings benefits for both individuals and nations. Beginning with biology, Stability and Change in Science Education: Meeting Basic Learning Needs explores this balance in teaching and learning science. The authors reflect upon this equilibrium as they each present their work and its contribution. The book provides a wide range of examples using the change/stability lens. Authors from the Netherlands, Israel, Spain, Canada and the USA discuss how they observe and consider both homeostasis and novelty in theory, projects and other work. The book contains examples from science educators in schools and in other science rich settings. Contributors are: Lucy Avraamidou, Ayelet Baram-Tsabari, Michelle Crowl, Marilynne Eichinger, Lars Guenther, Maria Heras, Phyllis Katz, Joy Kubarek, Lucy R. McClain, Patricia Patrick, Wolff-Michael Roth, Isabel Ruiz-Mallen, Lara Smetana, Hani Swirski, Heather Toomey Zimmerman, and Bart Van de Laar.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".