Bibliographic record
Abstract
INTRODUCTION: THE SCOPE OF MORAL PSYCHOLOGY, ANCIENT AND MODERN Moral psychology addresses itself to the interface between ethics and psychology. One of the basic principles of moral psychology is the apparently trivial one, that all ethically correct actions are, to begin with, actions: inasmuch as they are the deliberate or at least intentional actions of human beings, ethical actions will share features with the class to which they belong, and fall under whatever constraints belong to the larger kind. This of course raises an immediate question about the coherence of the topic so described. Psychology is clearly a descriptive field, and ethics is the normative field par excellence ; the one tells us how the human mind does function, the other tells us how human agents ought to act. Given this fundamental difference, we may not assume, without further argument, that the first discussion can place any constraints whatsoever on the second. The mere fact that psychology places limits on what is humanly possible does not show, without further argument, that ethics must keep its demands within those limits. The further argument tends to come, nowadays, in terms of a sort of mixing axiom of morality and modality, that the agent cannot be obligated to do anything it is not possible for the agent to do. This is usually abbreviated to the slogan that ‘ought’ implies ‘can’, though its teeth are more often bared in the contrapositive formulation, that ‘not possible’ implies ‘not obligatory’.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".