Nowhere at Home: Gender, Race and the Making of Anti-immigrant Discourse in\n Canada
Bibliographic record
Abstract
This paper -part of a larger project -traces some identifiable and gendered shifts in anti-immigrant/refugee discourses in Europe, the United States and Canada during the 1990s.My case study of Toronto, Ontario -in particular, recent racist portrayals of Somali refugee women as welfare cheats and efforts by anti-racist groups to articulate counter narratives of immigrant rights -suggests, respectively, critical links with racist constructions of California, especially immigrant Los Angeles, and the need to develop longer-term anti-racist strategies.RESUME Cet article, qui n'est qu'une partie d'un plus grand projet, retrace quelques discours identifiables et les changements dans I'equilibre des sexes, les tendances des discours anti-immigrants/refugies en Europe, aux Etats-Unis et au Canada, durant les annees 90.Mon etude de cas de Toronto, en Ontario, en particulier les descriptions racistes des refugiees somaliennes, disant qu'elles fraudent le bienetre social, et les efforts entrepris paries groupes anti-racistes pour proteger les droits des immigrants, suggerent, respectivement, qu'il y a des liens critiques entre les interpretations racistes de la Californie, surtout a Los Angeles oil il y a beaucoup d'immigrants, et le besoin de developper des strategies anti-racistes qui sont plus a long terme.The overweening, defining event of the modern world is the mass movement of raced populations, beginning with the largest forced transfer of people in the history of the world: slavery.The contemporary world's work has become policing, halting, forming policy regarding, and trying to administer the movement of people.Nationhood -the very definition of citizenship -is constantly being demarcated and redemarcated in response to exiles, refugees, Gastarbeiter, immigrants, migrations, the displaced, the fleeing and the besieged.The anxiety of belonging is entombed within the central metaphors in the discourse on globalism, transnationalism, nationalism, the break-up of federations, the rescheduling of alliances, and the fictions of sovereignty.-Toni Morrison, "Home" 2Imagine that you have forgotten your house keys.You go home and find a family of immigrants sleeping on your floor.What do you do?Call the police, of course, and have them removed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.051 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".