Bibliographic record
Abstract
From January 2017 to February 2019, approximately 42,000 asylum seekers have been intercepted by the RCMP while crossing the Canada-U.S. border. Asylum seekers intercepted by the RCMP represented just over one third of all asylum claims over this period. Another 70,800 other asylum seekers claimed protection at a Canadian port of entry upon arrival, or at inland office after having resided in Canada with a form of temporary work, study, or visitor status. By contrast, since January 2017, a little more than 607,500 permanent residents arrived in Canada through legal economic, family, and humanitarian channels. Recent polling suggests that border security is the most salient immigration issue among the Canadian public.4 Despite these perceptions, border crossers represent only a fraction of the total asylum claim backlog, an inventory of pending cases of people seeking protection in Canada on the basis of fear of persecution in their home country. In February 2019, 4,170 new asylum claims were registered, of which 808 individuals were intercepted at the Canada-U.S. border, making those border claims less than 20% of the total in that month.5 In August 2018, during summer months when border crossings tend to be higher, 4,965 new claims were registered, while the RCMP intercepted 1,747 asylum seekers crossing the border (35% of all claims in August 2018). The comparison of border crossings versus all claims by month is shown later in this report in Figure 5. One proposal currently being explored by the Canadian government to reduce crossings, is to renegotiate the “Safe Third Country Agreement” (STCA) with the United States. This policy brief outlines possible benefits and drawbacks of renegotiating the STCA. It begins with describing the current backdrop to renegotiation of the Agreement, including the rise and fall of border crossings, as well as how the STCA works in practice. It then describes reasons to why a renegotiated STCA may stem increases to the asylum claim backlog, while also outlining other potential impacts and downsides to a modified Agreement. It concludes by proposing alternative solutions to renegotiating the STCA that do not carry the same potential downsides.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 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".