Can We Teach the Intuitive Dog New Tricks? Reconciling Jonathan Haidt’s Viewpoint Diversity with His Moral Psychology
Bibliographic record
Abstract
In recent years, many scholars drew attention to political polarization in academia and attempted to reduce it. Jonathan Haidt, a social psychologist, suggests that his framework of “viewpoint diversity” can reduce polarization. However, this framework contradicts his earlier work, especially the “social intuitionist model.” On the one hand, he argues that reason is not crucial to changing someone’s mind. He uses the metaphor of a dog who makes intuitive decisions and wags its tail (reason) to communicate and justify them. On the other hand, he believes that scholars can change each other’s minds on political issues through reason. This paper seeks to reveal the tension between “viewpoint diversity” and the “social intuitionist model” and to reconcile it. In order to ground these frameworks into social psychological theories, this paper examines the social intuitionist model in relation to cognitive dissonance theory and suggests modifications to Haidt’s “viewpoint diversity” based on cognitive reappraisal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".