Science teachers as metascientists: an inductive–deductive dialectic immersion in northern alpine field ecology
Bibliographic record
Abstract
Efforts to promote more realistic conceptions about science are often limited by teachers’ inexperience in this domain. In this paper, we describe an ‘inductive–deductive, dialectic immersion’ approach towards assisting teachers in developing more realistic conceptions about science — along with corresponding revised perspectives about science teaching. Three secondary teachers of science with minimal science research experience engaged in a case study of science in action — specifically, in an episode of northern alpine ecological field research. Qualitative data analyzed by constant comparative methods suggested that these teachers shifted along a modernist through postmodernist continuum — as indicated by increased support for a more Naturalist epistemology of science, a more Antirealist ontology of science, and corresponding priorities towards science teaching and learning. Results suggest that teachers of science can develop postmodern views about science and science teaching if given opportunities to induce and deduce propositions about science in realistic cases of science in action.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".