Irish Nationalisms and Canadian Confederation
Bibliographic record
Abstract
“All the news just repeats itself,” runs a line in John Prine’s song\n“Hello in There.” Read Toronto’s newspapers, and you’ll find\nthat the city loses its innocence roughly every five years. Read\nCanadian history books or – if you have a strong enough constitution\n– watch Canadian history on television, and you’ll find\nthat Canada was “made” over and over again. Canada was made\non September 13th 1759, when the impeccably dressed General\nJames Wolfe scaled L’Anse au Foulon (with a little help from his\nfriends) and secured Canada for the British Empire. No, scratch\nthat. Stephen Harper assured us that it was made during the War\nof 1812. Or, if you’re John Ralston Saul, it was made by the Lafontaine-\nBaldwin alliance and responsible government in 1848. Not\nso, says Richard Gwyn; Sir John A Macdonald was “the man who\nmade us.” Roll over, Sir John; Justin Trudeau told us earlier this\nmonth that Canada was born at Vimy Ridge. Wrong again; we\nalso read that Tommy Douglas and Medicare gave Canada its\ndistinct identity. The country has had more remakes than Star\nTrek.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.015 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".