Olive Branch and the Maple Leaf: A Comparative Analysis of Refugee Policies in Canada and the United States and the Potential for Blended Reform
Bibliographic record
Abstract
This thesis reviewed the United States Refugee Admissions Program (USRAP) to address concerns regarding the program and actions taken by the Donald J. Trump administration. Specifically, the thesis sought to determine if the admission of refugees poses a threat to the United States and if the USRAP can be modified. To determine potential threats, the research reviewed several concerns, including physical threats that could be caused by refugee admissions as well as economic and social impacts that refugees could have on host countries or individual communities, and then weighed competing arguments against objective evidence. Additionally, the research made a broader comparison between the structure of the United States’ and Canada’s refugee programs to determine if best practices from both nations might craft an updated USRAP. Ultimately, the thesis determined that refugees pose neither a physical security risk nor an economic risk to the United States. The country, however, is currently failing in its statutory mandate to involve local communities in resettlement decisions; this has long-term negative effects on refugees and citizens. To address this concern, the United States should blend in smart practices from Canada’s Provincial Nominee Program to bring USRAP more in line with statutory requirements and current humanitarian needs.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".