A Mindful Approach to Teacher Education: An Interview With William Hare
Bibliographic record
Abstract
To honour and celebrate Dr. William Hare's important contributions to educational studies on the occasion of his retirement, a two-day conference was held at Mount Saint Vincent University, Halifax, Nova Scotia in October, 2008. As part of one of the working seminars, we interviewed William Hare. Beginning with questions on the complex concept of open-mindedness, he discusses the use of case studies in education, the controversial link between open-mindedness and neutrality, the challenging task for teachers of posing questions for which there are no definitive answers, and the central role he assigns the philosophy of education in teacher education. Hare also reflects on his lifelong commitment to the topic of open-mindedness and why it has fascinated him for so long. This interview and the video-taped format will be valuable educational resources for teachers and educators working in diverse discipline areas, including the philosophy of education.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.023 |
| 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".