Debunking the Narratives of Inclusion: Immigration Policy in Quebec, Canada, and the United States in the Age of Trump
Bibliographic record
Abstract
Narratives of inclusion and successful immigration stories permeate Canadian popular culture. Often compared as an equally desirable destination to the United States, Canada is frequently heralded as a refuge for immigrants; specifically, those seeking asylum. Frequently exalted as a morally superior nation to its southerly neighbors, Canada represents itself in the international arena as a country that celebrates, respects, and cares for all of its members—regardless of their sometimes precarious immigration status by emphasizing the multiculturalism of Canadian society. Such stories also permeate national history and therefore leave out the voices of the “other” and ignore episodes of overt discrimination. As such, this account of the Canadian narrative as a welcoming safe harbor is not always accurate as globalization and concerns over national security threaten to uproot the hegemonic perpetuation of racial stereotypes and justifications of exclusion. Illustrating these failures of the Canadian government to uphold its revered image, are the stories of Abdoul Abdi, a Somalian refugee who faced deportation after the Canadian government neglected to apply for his citizenship while he was a minor in the foster care system; and those of Haitian refugees once living in the United States with protected status crossing into Quebec by the hundreds in 2017 to seek asylum in Canada. With the aid of the two aforementioned case studies, explored through review of academic literature and news stories, this paper seeks to dismantle the commonly believed fallacies surrounding the immigration and refugee policies in the United States, Canada, and Quebec in the Age of Trump.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.076 | 0.034 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".