The harm principle and the greatest happiness principle: the missing link
Bibliographic record
Abstract
In this article I present a possible solution for the classic problem of the apparent incompatibility between Mill's Greatest Happiness Principle and his Principle of Liberty arguing that in the other-regarding sphere the judgments of experience and knowledge accumulated through history have moral and legal force, whilst in the self-regarding sphere the judgments of the experienced people only have prudential value and the reason for this is the idea according to which each of us is a better judge than anyone else to decide what causes us pain and which kind of pleasure we prefer (the so-called epistemological argument). Considering that the Greatest Happiness Principle is nothing but the aggregate of each person's happiness, given the epistemological claim we conclude that, by leaving people free even to cause harm to themselves, we still would be maximizing happiness, so both principles (the Greatest Happiness Principle and the Principle of Liberty) could be compatible.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".