A Deweyan Critique of the Critical Thinking versus Character Education Debate
Bibliographic record
Abstract
What distinguishes the philosophies of education advanced by pragmatists? Does pragmatism have something distinctive to offer contemporary philosophy of education? This paper applies these questions, which Randall Curren asks in “Pragmatist Philosophy of Education” (2009), to a more specific current debate in philosophy of education: the debate over educating for critical thinking, and/or for intellectual virtues. Which, if either, should be given priority in higher education, and why? This paper develops a Deweyan approach to these questions, inviting character content but also offering specific ways for educators and institutions to stay alert to the pedagogical and indoctrination concerns with character education initiatives, including those for intellectual virtues specifically.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".