Bibliographic record
Abstract
My paper challenges an influential distinction between pain and suffering put forward by physician-ethicist, Eric Cassell. I argue that Cassell's distinction is philosophically untenable because he contrasts suffering with an outdated theory of pain. In particular, Cassell focuses on one type of pain, the interpretation of nociception induced by noxious stimuli such as heat or sharp objects; yet since the late 1970s, pain scientists have rendered both nociception and noxious stimuli unnecessary for pain. I argue that this discrepancy between Cassell's distinction and pain science produces three philosophical problems for his distinction: first, he frames his distinction too generally, concentrating on only one type of pain (interpreted nociception) to the neglect of others, such as neuropathy; second, it is possible that Cassell's understanding of pain may include suffering; and third, Cassell gives examples of pain and suffering manifesting independently of each other, but it is possible that these cases may instead exemplify differences between nociceptive and non-nociceptive types of pain. Due to these problems, I conclude that Cassell's distinction currently lacks a difference. I call for new efforts to articulate the differences, if any, between pain and suffering.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".