Hume, Malebranche, and the Self-Justification of the Passions
Why this work is in the frame
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Bibliographic record
Abstract
In Book 2 of the Treatise , Hume echoes Malebranche’s Search after Truth in noticing that all our passions tend to justify themselves. I reveal this borrowing and examine how this phenomenon of the self-justification of the passions fits into Malebranche’s and Hume’s different approaches to the passions and their links to judgment and truth. I maintain that whereas Malebranche aims to warn against the errors that passions involve when justifying themselves, Hume only considers the self-justification of the passions as an example of the dynamic nature of the mind, other examples of which are displayed elsewhere in the Treatise . I also show that Hume’s understanding of the self-justification principle illustrates another important theme of Books 1 and 2, namely, the reciprocal influences of the imagination and the passions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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 it