Becoming an Activist Science Teacher: A Longitudinal Case Study of an Induction Intervention
Bibliographic record
Abstract
This longitudinal case study follows a new science teacher throughout his first 5 years of teaching in a publicly funded alternative high school. It explores the ways in which he reconciled learning to accommodate his professional and personal responsibilities with his ambition to be an activist science teacher. Framed as a form of professional development, the study examined his beliefs and the ways in which developing and discussing a professional belief web aided in his evaluation of the extent to which he realized his goals. The techniques used in this study have the potential to be used for new teachers‟ professional development and the case itself can aid in that endeavour. Correspondence concerning this article should be addressed to: Sarah Elizabeth Barrett, York University, Toronto, Ontario, Canada. Email: sbarrett@edu.yorku.ca
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".