The Border Papers Marching Together to Different Tunes
Bibliographic record
Abstract
In this issue... Canadian immigration authorities will have to toughen up their screening and monitoring of visitors to prevent terrorists and criminals from joining the large numbers of entrants to the country ever year. Canada and the United States should cooperate on border security, though there is no compelling reason for Canada to harmonize immigration selection with its neighbour. The Study in Brief In the wake of the terrible events of September 11, 2001, and in the context of an increasingly integrated and security-conscious North America, both Canada and the United States have revisited their immigration policies. Tightening up screening of immigrants and refugee claimants has been a significant focus for change in both countries. Tightening up screening and monitoring of visitors and other temporary entrants has also been a priority in the United States. This has not been the case in Canada, a particularly troubling omission given the expanding role envisaged for foreign temporary workers and international students. Indeed the lengthy processing and enhanced scrutiny accorded immigrants and refugee claimants may simply persuade would-be wrongdoers to seek their entry as part of the steady stream of foreign travelers who receive
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.060 | 0.018 |
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".