Revisiting New Zealand’s “Unfortunate Experiment”: Is medical ethics ever a thing done?
Bibliographic record
Abstract
An experiment dating from the 1960s in New Zealand has eerie similarities to research begun in 1976 in India. In both cases, women with evidence of early cervical cancer or pre-cancer went untreated, despite known treatments that could have prevented their condition from worsening. This Comment on carcinoma cervix research grew out of my reading of a new book by Ronald W Jones about the New Zealand experiment. Jones, a recently retired obstetrician/gynaecologist, worked at the hospital where the controversial research took place and was a whistleblower in the case. His book provides a meticulous account of internal struggles within the hospital over what has been called "the unfortunate experiment." Readers might fairly ask whether a detailed examination of a decades-old research scandal in New Zealand can usefully inform ethics debate in India today, where conditions are so different. I argue that Jones's account does indeed provide valuable insights for understanding research wrongdoing in other contexts, including low-income countries. Jones challenges some widespread assumptions about why such cases occur and how to combat them, as do several other recent analyses of research scandals.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.091 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 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".