Bibliographic record
Abstract
This particular chapter examines the role of business immigration lawyers, primarily within the more northern portion of the Cascadia region, and is the final chapter that reviews the empirical data for this study. A total of 23 semi-structured interviews took place with Canadian and US immigration attorneys who helped to facilitate the movement of high-technology professionals moving across the border under NAFTA and associated regulations. In regards to this study, immigration attorneys interviewed served since the early 1990s in a capacity as professional advocates and facilitators for NAFTA applicants, and in the contemporary period also had a wide breadth of knowledge about the labor mobility process and immigration law, especially when it involved crossing the US–Canada–Mexico borders. Consequently, this chapter seeks to understand the role of lawyers regarding how the Canada–US border operates in the Cascadia region and beyond. This component of the study was also significant as there is very little literature, if at all, on the role of business immigration attorneys in the movements of professional foreign workers across international boundaries. Thus, this chapter helps to provide more academic insights into the role and capacities that business immigration attorneys fill in the movement of professionals across North American borders.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".