Bibliographic record
Abstract
Canada has always been a trading nation. From the early days of fur and fish, to the present, when a remarkable ninety percent of our gross national product is attributable to exports and imports, Canadians have relied on international trade to bolster our economy. A Trading Nation, Michael Hart's brilliantly crafted overview and analysis of the historical foundations of modern Canadian trade policy, is the first survey to address the history of Canadian commercial policy in over fifty years. Taking the view that to understand the present and better prepare for the future, we must first comprehend the past, Hart skilfully guides readers through more than three centuries of Canadian trade history. His engaging narrative explains how Canadians, who currently enjoy one of the highest standards of living in the world, have largely come to accept that a country that derives much of its wealth from international commerce has much to gain from an open, well-ordered international economy. Close attention to trade and related economic policy choices, he argues, is crucial if Canada intends to adapt to the challenges of the new globalized economy. This bold and original study is a tour de force, evocative of Harold Innis's and Donald Creighton's pioneering works in the history of the nation. Hart’s experience as an active trade negotiator is reflected throughout this accessible and lively book. A Trading Nation is destined to become a classic of Canadian historical, economic, and political studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.043 | 0.012 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".