“We're taking their brilliant minds”: Science teacher expertize, meta‐discourse, and the challenges of teacher–scientist collaboration
Bibliographic record
Abstract
Abstract Programs that bring teachers and scientists together have continued to achieve attention as important potential contributions to science education. Often, however, those programs place teachers in the position of learner or apprentice to the scientists. This study examines a teacher–scientist partnership program (two project sites, two teachers and one scientist at each) that explicitly sought to challenge that model, bringing science teachers into science labs to share their expertize in a collaborative project. Using a framework that probes both the actors’ and analysts’ perspectives on expertize, this study examines whether the promise of the program's goal for mutual learning through collaboration can be met. Analysis of interviews and collaborative products focused on evidence of contributory and interactional expertize to understand if the project was able to bring together a group that was likely to be able to collaborate successfully. Metadiscourse was then probed to gain a deeper understanding of the ways in which they spoke of the expertize of their collaborators. Findings suggest that creating collaborative teams with ideal expertize matches is challenging and that collaborative efforts are complicated by the historic status of scientific and teaching expertizes especially in relation to outreach or knowledge translation projects.
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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 | 0.047 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| 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".