Bibliographic record
Abstract
INTRODUCTION The fact that one desires something does not suffice to establish that one ought to pursue it. The fact that a course of action appears good from a certain perspective does not guarantee that this course of action is in fact good. Insofar as the agent is rational, she evaluates the reliability of various perspectives on reflection and tries to come up with a coherent understanding of what she should pursue. Thus drawing on all particular perspectives, the agent forms a reflective perspective that underwrites what I call the agent's “general conception of the good.” Section 2.2 presents, and discusses the importance of, a notion of a general conception of the good. A perfectly rational agent would always act in accordance with his conception of the good. However, an imperfectly rational agent could have a certain general view of the good and yet act otherwise; an imperfectly rational agent could form an intention at odds with his general conception of the good. According to the scholastic view, what the agent judges to be good is the agent's intention in action. Given that the scholastic view identifies desires with appearances of the good in part because what makes them mere appearances is the fact that they ought to be evaluated from a reflective perspective, it is indeed natural for the scholastic view to identify intentions with judgments of the good. However, this kind of identification has been the subject of various criticisms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".