Bibliographic record
Abstract
Upon beginning their school-based teaching practica, teacher candidates enter a realm of practice that is at once uncertain, vulnerable, and particular, and where good educational practice centers on teacher's ability to see and to judge and to act ethically with others in concrete situations and ways. An important question for teacher education is: how do teacher candidates begin to acquire this capacity? This article draws on Martha Nussbaum's Aristotelian-inspired concept of “discernment” to explore how people begin to grasp phronesis or practical wisdom in teaching by becoming more responsive to the ethical character of classroom particulars while on practicum in ITE. The discussion is grounded in a teacher candidate's comments while on practicum at a Canadian elementary school to illustrate how becoming an ethical teacher is bound up in becoming perceptive, in opening oneself to the value and special wonder of the particular, in feeling the appropriate emotions about what one chooses, and by engaging in a play of thought about classroom particulars and educational universals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| 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".