Philosophical Questions about Teaching Philosophy: What's at Stake in High School Philosophy Education?
Bibliographic record
Abstract
What is at stake in high school philosophy education, and why? Why is it a good idea to teach philosophy at this level? This essay seeks to address some issues that arose in revising the Ontario grade 12 philosophy curriculum documents, significant insights from philosophy teacher education, and some early results of recent research funded by the federal Social Sciences and Humanities Research Council (SSHRC) in Canada. These three topics include curricular disputes, stories of transformation from philosophy student to philosophy teacher, and preliminary research findings. All underscore the importance and complexity of philosophy education, as well as its challenges and benefits, including the cross-curricular benefits philosophy education imparts to the study of other subject areas. Collectively, these serve as a springboard for asking some larger and broader philosophical questions about the teaching and learning of philosophy, and they demonstrate that this is a promising new area of study and of teaching for philosophers of education. I will raise some questions about philosophy that will help frame the next stage in the SSHRC research into the teaching and learning of philosophy in Ontario, and which I contend are new and fundamental questions to ask about philosophy itself.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.071 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| 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".