The Spanish influenza pandemic of 1918-19 : new perspectives
Bibliographic record
Abstract
Howard Philips and David Killingray Introduction Part I: Virological and Pathological Perspectives 1. Edwin D. Kilbourne A Virologist's Perspective on the 1918-1919 Pandemic 2. Jeffery K. Taubenberger Genetic Characterisation of the 1918 'Spanish' Influenza Virus Part II: Contemporary Medical and Nursing Perspectives 3. Wilfried Witte The Plague That was Not Allowed to Happen: German Medicine and the Influenza Epidemic of 1918-1919 in Baden 4. Nancy K. Bristow 'You Can't Do Anything for Influenza': Doctors, Nurses and the Power of Gender During the Influenza Epidemic in the United States Part III: Official Responses to the Pandemic 5. Geoffrey W. Rice Japan and New Zealand in the 1918 Influenza Pandemic: Comparative Perspectives on Offical Responses and Crisis Management 6. Mridula Ramanna Coping with the Pandemic: The Bombay Experience Part IV: The Demographic Impact 7. Wataru Iijima Spanish Influenza in China, 1918-1920 8. Kevin McCracken and Peter Curson Flu Downunder: A Demographic and Geographic Analysis of the 1919 Pandemic in Sydney, Australia 9. N. P. A. S. Johnson The Overshadowed Killer: Influenza in Britain in 1918-1919 10. D. Ann Herring and Lisa Sattenspiel Death in Winter: Spanish Flu in the Canadian Subarctic 11. Beatriz Echeverri Spanish Influenza seen from Spain 12. Patrick Zylberman A Holocaust in a Holocaust: The Great War and the 1918 'Spanish' Influenza Epidemic in France 13. Andrew Noymer and Michel Garenne Long-Term Effects of the 1918 'Spanish' Influenza Epidemic on Sex Differentials of Mortality in the USA: Exploratory Findings from Historical Data Part V: Long-Term Consequences and Memories 14. James G. Ellison 'A Fierce Hunger': Tracing Impacts of the 1918-1919 Influenza Pandemic in Southwest Tanzania 15. Myron Echenberg 'The Dog that Did Not Bark': Memory and the 1918 Influenza Epidemic in Senegal Part VI: Epidemiological Lessons of the Pandemic 16. Stephen C. Schoenbaum Tranmission of and Protections against Influenza: Epidemiological Observations Beginning with the 1918 Pandemic and the Implications
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".