Critiquing Science Lessons for their Authenticity as a Means of Evaluating Teacher-Candidate Understanding of the Nature of Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This inquiry uses teacher-candidate critiques of science lessons as a means of evaluating the effectiveness of an instructor's pedagogical approach to promote teacher candidate understanding of the nature of science (NOS) in a science methods course. Three cohorts of teacher candidates enrolled in a middle-years teacher education program were required to evaluate science lessons for their authenticity based on their developing understanding of the nature of science. The results indicated that the most detailed and accurate analysis of the authenticity of the lessons occurred in the cohort where the nature of science was developed (a) explicitly throughout the course, (b) through the use of exemplars of historical science development, and (c) through a setting that encouraged collaborative consideration of the nature of science.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it