A <scp>Perspective‐Taking</scp> Theory of <scp>Open‐Mindedness</scp>: Confronting the Challenge of Motivated Reasoning
Bibliographic record
Abstract
ABSTRACT Previous research on open‐mindedness, whether by critical thinking researchers, philosophers of education, or intellectual virtue theorists, has not sufficiently addressed the challenge posed by cognitive bias, particularly motivated reasoning. How can students develop their open‐mindedness when they are often motivated to preserve their prior beliefs? In this paper, James Southworth proposes a theory of open‐mindedness that attempts to confront the problem of motivated reasoning. He argues that to be open‐minded is to be receptive to ideas that challenge one's prior beliefs. He discusses two central and interrelated aspects of receptivity: (1) a motivation toward the truth, and (2) an ability and disposition to take on the perspective of an individual(s) who holds an opposing belief. While open‐mindedness includes cognitive, affective, and motivational dimensions, Southworth argues that it is narrow in scope. Open‐mindedness does not concern neutral scenarios. Moreover, open‐mindedness does not require the ability to reason well. Southworth concludes by considering some practical implications of this account with respect to reading and writing practices in higher 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.043 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".