Bibliographic record
Abstract
Perceptualists maintain that emotions essentially involve perceptual experiences of value. This view pressures advocates to individuate emotion types (e.g. anger, fear) by their respective evaluative contents. This paper explores the Attitudinalist Challenge to perceptualism. According to the challenge, everyday ways of talking and thinking about emotions conflict with the thesis that emotions are individuated by, or even have, evaluative content; the attitudinalist proposes instead that emotions are evaluative at the level of attitude. Faced with this challenge, perceptualists should deepen their analogy with sensory experience; they should distinguish types of emotions by their content much as we can plausibly distinguish types of sensory experience (e.g. visual, auditory) by theirs. A second lesson is that perceptualists should distinguish an emotion’s representational guise (uniform across emotions) from its formal object (which varies).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".