Canada's Catch-22: The State of Canada-US Relations in 2014
Bibliographic record
Abstract
Economic and political relations between Canada and the United States, our most important foreign relationship, have worsened since the Fraser Institute’s previous report on the state of Canada-US relations, Skating on Thin Ice (Moens, 2010). Canadian merchandise exports to the United States have weakened in relative terms. At the same time, there is not enough Canadian export diversification to other destinations to make up this relative loss.American and Canadian regulators are talking about rationalization, mutual recognition, and harmonization regarding certain policy areas, manufacturing product standards, and border security regulations. Incremental progress is being made, mainly by pilot projects, and it appears that border costs have plateaued. Capping costs is good, but the goal remains to reduce border costs. Border crossing data, though incomplete, suggests that Americans are traveling far less to Canada and that the commercial costs of crossing the border remain too high.While Canadians are selling relatively less to the US than they buy from the US, they are also not selling substantially more to the rest of the world. In other words, Canada’s record for diversifying its trade from the US is modest over the last five years. The conclusion of the Comprehensive Economic and Trade Agreement (CETA) with the European Union offers a small trade effect for Canada and also promises an important advance in lowering certain regulatory barriers to trade.The Ryan-Murray budget accord will offer another two years of modest budget cuts in the USA, and will likely improve policy stability and thus foster GDP growth. Canada’s opportunities to benefit from a stronger US economy in 2014/15 exist, but Canadians cannot count on any policy governance from the bilateral relationship to facilitate such market growth. The Catch-22 of Canada’s ongoing trade dependency with the USA and its modest success in diversification can only be lessened by strategic and systematic attention from the US Executive branch. In such a strategy, a two-pronged approach would emerge. It involves both lowering North American barriers to trade and negotiating freer trade with the rest of the world as a North American economic actor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".