Bridging critical thinking and transformative learning: The role of perspective-taking
Bibliographic record
Abstract
Although the literature on critical thinking and transformative learning has remained relatively distinct, they have both emphasized the importance of working through and resolving states of doubt. There has been less focus, however, on how we can bring ourselves from a confirmed belief to a position of doubt. This is a foundational skill. Without it, the possibility for intellectual and personal growth is limited. In part one, I focus on critical thinking to investigate what ability and/or disposition can help thinkers arouse a state of doubt. I first consider traditional dispositions of critical thinking, specifically reflection and open-mindedness, and argue that they are largely ineffective as they do not confront the problem of cognitive bias. I then propose perspective-taking as an essential tool to bring about a position of doubt. In part two, I examine leading theorists in transformative experience, transformative education, and transformative learning, who have also largely neglected perspective-taking. I illustrate that perspective-taking can initiate some instances of transformative learning and thereby provides a connecting point to critical thinking. Nevertheless, when engaging with perspective-taking exercises, I argue that instructors ought to prioritize the development of students’ critical thinking skills. In part three, I focus my discussion on incorporating nonfiction perspective-taking readings into university course syllabi as a way to develop students’ critical thinking while creating the conditions for transformative learning.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| 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".