Bibliographic record
Abstract
I have benefited from the contributions of many individuals who have aided me over the years in my research for this study, who have commented on drafts of one or more parts of it, and who in all cases have provided me with general advice and encouragement.In particular, I am grateful to a number of past and present archivists and historians from Library and Archives Canada, including Paul Marsden, Robert MacIntosh, Tim Dubé, Myron Momryk, Robert Fisher, and Tim Cook (now of the Canadian War Museum), and Carl Christie, Serge Bernier, and Stephen Harris of the Directorate of History and Heritage, Department of National Defence.George Henderson and the rest of the staff at the Queen's University Archives, along with that of the Provincial Archives of Nova Scotia (now Nova Scotia Archives), made it a pleasure for me to visit both Kingston and Halifax in the early years of my research, to view the papers of several former members of the federal Cabinet and other individuals.More recently, Carol Reid and others at the Military History Research Centre of the Canadian War Museum have been just as co-operative
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.328 | 0.152 |
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".