Bibliographic record
Abstract
NAFTA as well as all other trade agreements create a debate over labor mobility provisions as a means of facilitating trade. In particular, temporary visa provisions in NAFTA increased the mobility of highly skilled Canadians to the United States. The resulting brain drain was unanticipated and complemented the rapid growth in bilateral trade and the net flow of financial capital from Canada to the United States in the 1990?s. This human capital outflow became a major policy issue by the mid-1990?s in Canada. Concerns over lost public finance and growth opportunities coupled with an inability to replace this lost manpower with traditional third world immigrants fuelled the debate in Canada. This paper suggests two policy tools to mitigate the impact of this NAFTA induced brain drain to the United States from Canada without reducing mobility provisions in the trade agreement. First a Canadian post-secondary contingent student loan scheme should be instituted to offset the value of the lost Canadian human capital and a private based immigrant recruiting mechanism in Canada is suggested to improve the quality of Canada?s replacement immigrants.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".