How Demographic Transition Can Help Predict Canada-US Trade Relations in 25 Years
Bibliographic record
Abstract
This article seeks to predict some aspects of future Canada-United States trade relations by focusing on the demographic outlook for North America in 25 years. It argues that this demographic profile can be known with reasonable certainty in 2017, allowing policy-makers to take actions today to prepare for that future. I argue that North American policy-makers can sow the seeds for a more prosperous economic future in 25 years by focusing now on the shared economic future they wish to build. I outline several ways in which renegotiations of the North American Free Trade Agreement can be used to prepare Canada and the US for the future economy, with an emphasis on Canada's comparative advantage in education. I also identify several sectors that could benefit from greater bilateral integration in order to protect and increase employment on both sides of the border in a future economy characterized by technology, automation, and services-based employment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".