Bibliographic record
Abstract
There is little consensus concerning the truth or reference conditions for evaluative terms such as “good” and “bad.” In his paper “Good and Evil,” Geach (1956) proposed that we distinguish between attributive and predicative uses of “good.” Foot (2001), Thomson (2008), Kraut (2011), and others have put this distinction to use when discussing basic questions of value theory. In §§1-2, I outline Geach’s proposal and argue that attributive evaluation depends on a prior grasp of the kind of thing that is evaluated, which is another way of saying a prior grasp of a thing’s nature. In §§3-4, I discuss the evaluation of artifacts, which provide the clearest examples of attributive evaluation. This allows me to address a series of problems apparently facing the idea of attributive goodness. In §5, I consider the neo-Aristotelian idea that we can extend attributive accounts of goodness to human lives, and I pay attention to Foot’s account of natural goodness. This leads me to consider the goodness of human life as a whole in §6. At this point. I depart from Geach’s approach and argue that questions of attributive goodness finally give rise to questions of predicative or absolute goodness.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".