Bibliographic record
Abstract
Introduction When we make choices, even such everyday choices as how to spend an evening (or what restaurant to choose beforehand, or what mode of transport to use in getting there), we are typically confronted with a plurality of different criteria informing the choice. In choosing, for example, between spending the evening at the movies or at a philosophy lecture, we might have to consider whether on this particular evening we wish to be pleasurably entertained or intellectually challenged. There might also be good reason for considering cost, convenience of parking, and the amount of time involved. Moreover, these various criteria seem to flow from quite different and independent sorts of values. It seems hard to believe that the purely hedonic pleasure involved in watching some action film is really an aspect of a larger philosophical understanding, or that the understanding offered by the philosophy lecture, although pleasurable, is the same kind of pleasure as that offered by the movie. Indeed, it seems likely that the last three considerations mentioned (cost, convenience, time), which are of a more practical nature, will be as much informed by how we might otherwise spend our time and money, that is, by the broad range of very different forms of experience that could be enjoyed beyond the two options mentioned here, and thus that they implicate an even more diverse array of criteria and values than pleasure and understanding. Plurality, then, is the rule rather than the exception when we make choices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".