Teaching about ethics through socioscientific issues in physics and chemistry: Teacher candidates' beliefs
Bibliographic record
Abstract
Abstract The purpose of this qualitative study was to identify and explain the origins of physics and chemistry teacher candidates' beliefs about teaching about ethics through socioscientific issues (SSI). This study utilized a series of in‐depth interviews, while the participants ( n = 12) were enrolled in a 9‐month teacher education program at an urban university in Canada. Our data analysis revealed that beliefs about teaching physics and chemistry using SSI derive from a complex web of fundamental beliefs exemplified by four archetypes representing the subject‐specific identities of our teacher candidates—Model Scientist/Engineer, Model Individual, Model Teacher, and Model Citizen. Furthermore, we found that the justification for belief change required by a particular teacher candidate depends on these subject‐discipline identities. Thus, the presence of each archetype in preservice classrooms has ramifications for the way a teacher educator should encourage his or her students to include SSI in their teaching. © 2009 Wiley Periodicals, Inc. J Res Sci Teach 47: 380–401, 2010
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".