An intellectual history of suffering in the Encyclopedia of Bioethics, 1978–2014
Bibliographic record
Abstract
Suffering is an important theme in many bioethical debates, yet little historical research is available to contextualise ideas about it. My article proposes a preliminary intellectual history of suffering in bioethics using the field’s most trusted tertiary work, the four editions of the Encyclopedia of Bioethics (1978–2004), later renamed Bioethics (2014). In the first edition, I find suffering roughly conceptualised as either the negation of a good or as a pain. The former acquired a technical connotation beginning in the second edition, when physician Eric Cassell refined the negative aspects of suffering into a full-fledged theory. Now, suffering no longer marked the loss of just any good but instead threatened one’s purpose in relation to that good. Cassell also strongly distinguished suffering from pain which, when combined with his theory of suffering, hardened earlier distinctions between pain and suffering that were present but weak in the first encyclopedia. Both Cassell’s theory and his strong distinction impacted how other contributors moralised suffering in the later encyclopedias, although his influence was not total: utilitarians continued to moralise suffering in ways that still roughly construed it as pain. Consequently, Cassell and the utilitarians conflicted conceptually. Nevertheless, this tension went unfelt in the encyclopedias for reasons I describe. I close by suggesting areas for further historical research and argue for their relevance to bioethical enquiries into 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".