Bibliographic record
Abstract
f e w d a y s a f t e r the Great War broke out in 1914, Canada's acting High Commissioner in London, George Perley, MP for the Quebec constituency of Argenteuil-Les Deux Montagnes and temporary replacement for the late Lord Strathcona, received a polite suggestion from the British colonial secretary: "W hy not raise a Royal Montcalm Regiment in Canada?It might associate the name of Montcalm and the Province of Quebec specifically with an Empire War?" Perley dutifully transmitted the message to his prime minister, Sir Robert Borden but adding no endorsement for the idea: "personally doubt wisdom of doing anything to accentuate different Races as all are Canadians."Perley was as blissfully unaware as the Prime Minister about how profoundly the Great War would tear Canada's founding nations apart and doom his own party to Liberal dominance for the rest of the Twentieth Century.While Montcalm was better remembered in Quebec as a loser rather than a proud link to an embattled France, Perley and Borden would have been wise to consider how best to engage Quebecois in a war proclaimed by Great Britain without any consultation with Ottawa or any other dominion or colony in a world-wide Empire.O f course, when he sent on the message, Perley seemed to be right.The news of war inspired a popular excitement that was
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".