Justice Principles, Empirical Beliefs, and Cognitive Biases: Reply to Buchanan's ‘When Knowing What Is Just and Being Committed to Achieving it Is Not Enough’
Bibliographic record
Abstract
ABSTRACT This article raises three concerns about Buchanan's argument related to the individualist description of ideology and psychological description of the obstacles to justice, as well as the way in which he separates empirical and normative beliefs, which, the article argues, are much more closely connected in all the examples that he raises. In the end, however, it agrees with Buchanan's central contention concerning the cognitive biases that interfere with progress towards justice, but, it argues, these operate at a more sub‐conscious level than described by Buchanan.
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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 | 0.030 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".