Bibliographic record
Abstract
Philosophers have tried very hard to show that we must be virtuous to be happy. But as long as we stick to the modern understanding of happiness as something experienced by a subject – and I argue against contemporary eudaimonists that we should indeed do so – there can at best exist a contingent causal connection between virtue and happiness. Nevertheless, we have good reason to think that being virtuous is non-accidentally conducive to happiness. Why? First, happiness is roughly the experiential condition of enjoying predominantly positive affective phenomenal states concerning things that are subjectively important to us. I argue that this straightforward sentimentalism about happiness has several advantages over Daniel Haybron’s emotional condition account. Second, insofar as we’re virtuous, we can correctly identify what is worth doing in our particular situation and will skillfully pursue it. At the same time, we’re not bothered by things that are not worth caring or worrying about. Consequently, virtuous people are likely to enjoy central positive emotions related to success and approval by others, and avoid common negative emotions related to social comparison or avarice. While their happiness is still in part a matter of luck, it is such to a lesser degree than for the rest of us.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".