THE DRAFT NATIONAL POLICY AND ITS IMPACT ON STRENGTHENING CUSTOMS PROTECTION IN THE DOMINION OF CANADA 1873-1878
Bibliographic record
Abstract
In 1873, a wave of economic depression swept across the entire North American continent, and it was one of those crises whose impact extended to several countries, as it did not originate in Canada and was not limited to its borders. The Dominion of Canada was less able to withstand industrial pressures than its neighbor, the United States of America, which The severity of the economic crisis increased significantly, and the policy of protection in that country did not give hope to the Canadian factory, so the crisis began to paralyze farmers, wood exporters, and others. That prompted John A. Macdonald (John A. Macdonald) to adopt a policy he called (the magic wand policy), to move the country and attract people to it again, so he put forward the idea (national policy), through which the subject of our research came (the national policy project and its impact on strengthening customs protection in Dominion of Canada 1873-1878), project knowledge and its impact on strengthening the economic situation in Canada. The research included an introduction, two axes and a conclusion. The first axis dealt with the Canadian Conservative Party's adoption of the draft national policy in 1873, while the second axis shed light on the 1876 elections and the implementation of the national policy and its impact on strengthening protection as a complex.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".