Preface to the First Edition
Bibliographic record
Abstract
New books are expected to break new ground.This one is merely a new approach to an old subject.A generation ago the study of staple production in the eighteenth and nineteenth centuries attracted some of the best minds that have ever turned to Canadian history.It inspired, as we all know, some of the great books in the field, including H.A. Innis' seminal work on the fur trade and cod fishery, A.R.M. Lower's studies of the timber trade (to which he has recently added another volume), and of course D.G. Creighton's masterpiece, The Commercial Empire of the St. Lawrence.These were the books that led me to wonder about the politics of new staple development.In the intervening decades new questions drew historians away from the study of the relationship of political and economic phenomena well before the subject had been exhausted.As a result, much exploratory and descriptive work was left undone.Perhaps the most conspicuous gap in research lay in the twentieth century.Historians had studied the ways in which the problems of producing the old staples had laid down the warp and woof of Canadian politics in the nineteenth century, but no extended treatment had been accorded the political dynamics of the new
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.614 | 0.421 |
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".