Bibliographic record
Abstract
Canadian prosperity critically depends on the maintenance of an open and secure border between Canada and the United States. The reality is that 70 percent of our international trade is with the United States and that production has become highly integrated with value chains running back and forth across the border, sometimes many times. Even though it has become almost platitudinous to say that Canada and the United States have the world's longest undefended border, that does not necessarily mean that the border has not been an obstacle impeding the flow of trade, investment and people between the two countries. Over the years, Canadian governments have faced many challenges keeping the border open and have successfully approached the US government. Engaging the new Obama Administration in the United States to ensure a more open and secure border for trade, investment and people must be a high priority for the Canadian government. The Thickening of the Border after September 11 Even though the border was reopened quickly following the September 11th attacks, it was not the same as it had been. Under first the Canada-US Free Trade Agreement (FTA) and subsequendy the North American Free Trade Agreement (NAFD\\), Canadian exports to the United States
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".