“Welfare-For-Weapons”: Race, Criminality, and Somali Arrival in Neoliberal Times
Bibliographic record
Abstract
The 1990s in Canada were marked by neoliberal campaigns that stigmatized recipients of welfare. Elected officials and media suggested that welfare fraud was rampant, and every person on welfare needed to be surveilled. At the same time, fears of “illegitimate” refugees with no identification documents, dominated public discourse. As a result, every potential refugee was scrutinized for signs of fraud and criminality. Somali communities in Canada bore the brunt of this discourse. Utilizing the methodological and analytical frameworks of document analysis and critical discourse analysis, this dissertation is a study of 1990s welfare and immigration fraud discourses in Canada, and their continued effects on Somali communities in Canada today. Realized through a Black Muslim analytic, I engage popular print media and government archives, to examine how power manifests in discourses, the formation of knowledge, and the marginalization of Somali subjects – particularly in the construction of the racial imaginary of Canada in the 1990s. Additionally, I argue that the vilification of Somalis during the 1990s, were foundational to “war on terror,” discourses and the continued policing of Somali communities today. This study reveals the social, political and pedagogical implications of media and government documents on the lives of Somalis, as a relatively recent Black diaspora in Canada. Alongside, print media and government archives, this dissertation explores the community archives of Somali organizations and allied groups, to provide a counter-narrative to official and dominant discourses on Somali people, centering the stories of resistance by Somali communities.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.056 | 0.042 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".