<i>Techne,</i> a virtue to be thickened: Rethinking technical concerns in teaching and teacher education
Bibliographic record
Abstract
This article brings stories of teaching and learning to teach in China into conversation with Aristotle’s intellectual virtue techne and its reinterpretations. The intent is to challenge the overwhelming trend of instrumental rationality in teaching and teacher education in both China and Canada. I explore and thicken the concept of techne, one of the Aristotelian intellectual virtues, to understand what is at stake in today’s technical approaches to teaching and to imagine alternative possibilities. Aristotelian conception of techne is often translated as technical expertise, craft or skills and could to some extent justify today’s enthusiasm around technical concerns in teaching and teacher education. However, some of its contemporary re-appropriations critique and extend the restricted understanding of techne and offer educators a richer, more ethical view of techne and technical thinking in education. An interplay of Aristotelian intellectual virtues of techne and phronesis (practical wisdom) may reconnect techne to the rough ground of experience, challenge its preoccupation with instrumental ends, and assert its moral dimension.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.080 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".