What really is the nature of suffering? Three problems with Eric Cassell’s concept of distress
Bibliographic record
Abstract
Eric Cassell famously defined suffering as a person's severe distress at a threat to their personal integrity. This article draws attention to some problems with the concept of distress in this theory. In particular, I argue that Cassell's theory turns on distress but does not define it, which, in light of the complexity of distress, problematizes suffering in three ways: first, suffering becomes too equivocal to apply in at least some cases that Cassell nevertheless identifies as suffering; second, Cassell's account does not explain what sort of experience suffering is, resulting in theoretical and practical difficulties in distinguishing it from other medical conditions; third, there is good reason to believe that, in medical contexts, 'distress' just means 'suffering' or some cognate concept not yet distinguished from it, rendering Cassell's theory circular. I consider a rebuttal to my objections and reply, concluding that Cassell's theory of suffering needs a definition of distress to settle what the nature of suffering really is.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.084 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.020 |
| 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".