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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".