Suffering is bad: experiential understanding and the impossibility of intrinsically valuing suffering
Bibliographic record
Abstract
Abstract Suffering, I argue, is bad. This paper supports that claim by defending a somewhat bolder-sounding one: namely that if anyone—even a sadistic ‘amoralist’—fully understands the fact that someone else is suffering, then the only evaluative attitude they can possibly form towards the person’s suffering as such is that of being intrinsically against it. I first argue that, necessarily, everyone is disposed to be intrinsically against their own suffering experiences, holding fixed their specific overall degree of emotional aversiveness, because any evaluative attitude other than ‘being against’—including mere indifference—would in certain key circumstances make our suffering less emotionally aversive and thus different from the suffering experience (stipulatively) at issue. Second, fully understanding that someone else is having a given experience—Mary’s experiencing a vividly blue sky, say, or Job’s experiencing heart-rending grief—requires that we represent experientially their very instance of that experience-type (it requires, in other words, token phenomenal concepts). The result is that what goes for our own suffering goes for others’, too: maintaining an accurate experiential representation of the fact that someone else is having a suffering experience with a specific degree of overall emotional aversiveness is only compatible with coming to be intrinsically against their suffering. So suffering is—‘objectively’—bad: it’s only possible to respond with indifference towards anyone’s suffering if we don’t fully understand that they are suffering in the first place.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.052 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".