Bibliographic record
Abstract
This paper reviews Florence Nightingale’s contribution to the use of statistics to save lives, beginning with the Crimean War (1854-56). It addresses accusations to the contrary, that her work resulted in lives lost, with primary source data in refutation. It also demolishes exaggerated claims for her, on the extent and speed of death rate reductions achieved, that she collected statistics to this end, and that she did the work virtually single-handedly. Comparative French death rates during the war are cited which show how successful the British were with their sanitary reforms. Nightingale’s significant collaboration with the leader of the Sanitary Commission is related. The two went on to numerous successful reforms post-Crimea. The creation of a Statistical Branch was a key part of the strategy. Several unsuccessful attempts Nightingale made to improve statistics are noted, beginning with a rejected proposal to add questions on health to the 1861 Census. Next came the Colonial Office’s failure to follow up on her research on excessive deaths in British colonial hospitals and schools, which raised the broader issue of declines in aboriginal numbers. Finally, she had to give up on an attempt to have applied statistics taught at Oxford University, for the benefit of future Cabinet ministers and senior administrators. The paper argues that Nightingale’s belief that statistics can be used to save lives still has merit, so long as the endeavour is taken seriously, with adequate attention to detail and complexity.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".