Science and the Pacific War : science and survival in the Pacific, 1939-1945
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preface. Introduction: Science, Technology and the War in the Pacific R. MacLeod. Part I: The Scientists go to War. 1. Combat Science: OSRD's Postscript in the Pacific R. MacLeod. 2. The Smithsonian Goes to War: The Increase and Diffusion of Scientific Knowledge in the Pacific P.M. Henson. 3. Malaria in the Southwest Pacific in World War II M.E. Condon-Rall. 4. The Machine in the Pacific: The Diverse Legacy of Technology D.T. Fitzgerald. 5. The Role of Botanists During World War II in the Pacific Theatre R.A. Howard. Part II: The War Down Under. 6. Australian Universities at War: The Mobilisation of Universities in the Battle for the Pacific M. Freeman. 7. Australia's Mustard Gas Guinea Pigs B. Goodwin. 8. Technological Transfer and the War in the Pacific I.D. Rae. 9. Managing the Impact of War: Australian Anthropology and the South West Pacific G.G. Gray. 10. New Zealand Scientists in Action: The Radio Development Laboratory and the Pacific War R. Galbreath. Part III: The Unseen War. 11. Canadian Scientists, CBW Weapons and Japan, 1939-1945 D. Avery. 12. The American Cover-up of Japanese Human Biological Warfare Experiments, 1945-1948 S.H. Harris. 13. The Role of Scientific Intelligence in the Pacific War F. Cain. 14. The Useful War: Radar and the Mobilization of Science and Industry in Japan M.F. Low. Bibliography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it