People recognize and condone their own morally motivated reasoning
Bibliographic record
Abstract
People often engage in biased reasoning, favoring some beliefs over others even when the result is a departure from impartial or evidence-based reasoning. Psychologists have long assumed that people are unaware of these biases and operate under an "illusion of objectivity." We identify an important domain of life in which people harbor little illusion about their biases - when they are biased for moral reasons. For instance, people endorse and feel justified believing morally desirable propositions even when they think they lack evidence for them (Study 1a/1b). Moreover, when people engage in morally desirable motivated reasoning, they recognize the influence of moral biases on their judgment, but nevertheless evaluate their reasoning as ideal (Studies 2-4). These findings overturn longstanding assumptions about motivated reasoning and identify a boundary condition on Naïve Realism and the Bias Blind Spot. People's tendency to be aware and proud of their biases provides both new opportunities, and new challenges, for resolving ideological conflict and improving reasoning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".