Virtues, Teacher Professional Expertise, and Socioscientific Issues
Bibliographic record
Abstract
This article develops the notion that virtues can be utilized as a means of understanding the professional expertise that science teachers demonstrate when they deal with socioscientific issues. Socioscientific issues are those contentious issues that connect science to the society in which it operates— environmental issues being a prime example. We begin by accepting that both the cognitive and the affective facets of teaching represent a profes- sional expertise that can be discussed using a moral language of virtues. These virtues are trust, care, courage, honesty, practical wisdom, and fair- ness. In developing our notion, we draw on the work of Zeidler, Sadler, Simmons, and Howes, who have argued that an exploration of four peda- gogical elements (the nature of science, classroom discourse, cultural issues, and case-based issues) constitutes a research-based model for addressing moral education in the context of science education. It is our purpose to investigate the ecological validity of using virtues as descriptors of teacher professional expertise within each of these issues.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".