Canada before the effects of dragging of the American financial crisis
Bibliographic record
Abstract
This article analyzes the effects of the U.S. crisis on a country like Canada, which along with Mexico, enjoys an exceptional situation due to its geographical proximity to the United States, as well as the particularities of its economy, highly dependent on this country and regulated largely by its participation in NAFTA. The analysis of the Canadian case is relevant, since the effects of the U.S. crisis impacted unevenly on certain economic sectors and specific provinces and regions; this aspect contributed to the crisis did not have such devastating effects, but could not prevent the collapse of the market for asset-backed commercial paper. Although Canadian crisis was linked to the effect of the U.S. housing bubble, the effect was attenuated by the government policy of promotion housing, which does not subsidize the Canadian mortgage as in the United States. The disparity in the payment of interest during the loan period, directly affects the property rights of debt, so that in Canada the deadlines to be shorter cause the debt incurred will be paid into the bank in the first instance, while in the United States debt is renegotiated and transferred to other entities because the loans until their maturity is still a business that generates profits. Banks established in Canada, during the crisis, benefited from the bailout programs offered by the U.S. Federal Reserve, the Bank of Canada and the Canada Mortgage and Housing Corporation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".