Bibliographic record
Abstract
I should have kept a list.I have many people and organizations to thank and I'm worried that one or more might slip my memory.I'll do my best.To start, I could not have conducted the research for this book if it were not for the financial support of the Social Sciences and Humanities Research Council (SSHRC) and the Academic Research Programme (ARP) at the Royal Military College of Canada.Funds from these two granting agencies allowed me to spend many months in archives and many weeks walking far-away battlefields.Once the research and the writing were done, the Aid to Scholarly Publications Program (ASPP) supported the production and publication of my manuscript.I am grateful for the support of all three organizations.I am also grateful that they exist.Many friends and colleagues have guided me in my research and helped shape the final product.I'm in their debt.Jack Granatstein has been a tremendous friend and mentor -passing on research material, reading the entire manuscript, providing well considered criticism where necessary, and offering encouragement when needed
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.538 | 0.286 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".