A Middle Power on the World Stage: Canadian Grand Strategy in the Twentieth Century
Bibliographic record
Abstract
Good afternoon, ladies and gentlemen. Thank you for coming. Before I say another word, I’d like to thank David Bercuson for this great honor to be here today and Nancy Pearson Mackey for all the hard work that she has done to help me get here. I’d like to begin with three quick caveats. The first, as I reminded David when he invited me to give this lecture, is that I am not an historian of Canada. David seemed to think that my lack of formal training in this field would not pose an insurmountable obstacle to my delivering such a distinguished lecture in Canadian history, and I am putting my faith and trust in him that this is in fact so. It is not my intention here today to tell Canadians anything about their own history that they do not know. It is instead to perform the task David has given me: to place the history of Canada at war in the 20th century into a wider context and to train an outsider’s eye onto the problems of Canada and its approach to war and strategy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".