Bibliographic record
Abstract
Abstract The International Association for the Study of Pain’s (IASP) definition of “pain” defines it as a subjective experience. The Note accompanying the definition emphasizes that, as such, pains are not to be identified with objective conditions of body parts (such as actual or potential tissue damage). Nevertheless, it goes on to state that a pain “is unquestionably a sensation in a part or parts of the body, but it is also always unpleasant and therefore also an emotional experience.” This generates a puzzle that philosophers have been well familiar with: how to understand our utterances and judgments attributing pain to body parts. (The puzzle is, of course, general extending to all sensations routinely located in body parts.) This work tackles this puzzle. I go over various options specifying the truth-conditions for pain-attributing judgments and, at the end, make my own recommendation which is an adverbialist, qualia-friendly proposal with completely naturalistic credentials that is also compatible with forms of weak intentionalism. The results are generalizable to other bodily sensations and can be used to illustrate, quite generally, the viability of a qualia-friendly adverbialist (but naturalist and weakly intentionalist) account of perception.
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 | 0.002 | 0.007 |
| 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 | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".