When Will Your Consequentialist Friend Abandon You for the Greater Good?
Bibliographic record
Abstract
According to a well-known objection to consequentialism, the answer to the preceding question is alarmingly straightforward: your consequentialist friend will abandon you the minute that she can more efficiently promote goodness via options that do not include her maintaining a relationship with you. The most prominent response to this objection is to emphasize the profound value of friendship for human agents and to remind critics of the distinction between the theory’s criterion of rightness and an effective decision-making procedure. Whether or not this response is viable remains a contentious issue within the now considerable literature generated on the topic, yet it is a curious fact that the debate has unfolded in such a way that the question of when a consequentialist agent ought to break from her indirect methods of promoting the good and revert back to a direct form of consequentialist decision-making has not been decisively settled. In this paper, I claim that the empirical considerations at stake for resolving this question are more complicated than is normally acknowledged; however, I argue that this should not deter sophisticated consequentialists from endorsing flexible psychological dispositions in order to monitor these empirical considerations as best as can be expected for agents with our distinctly human faculties and limitations.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".