Media discourse and paradigm shifts in Canadian refugee and child policy frameworks
Bibliographic record
Abstract
This study draws on sociological theories of education and the methodological frameworks of Critical Discourse Analysis and Frames Theory and Analysis in order to examine print media representations of new immigrant, refugee, and precarious status children in the three Canadian newspapers, the Globe & Mail, National Post, and Toronto Star in the historical period of 1989-2009. The objective of this study is to analyze the ways in which media discourse provides ideological legitimacy to exclusionary immigration and refugee policies and the denial of social rights, and to identify media support for immigration justice campaigns. The historical period provides a context for the case study of the Toronto District School Board adopting a Don't Ask Don't Tell Policy in 2007 so that children without immigration status would be able to access schooling without the fear of being reported to immigration authorities. The educational experiences of new immigrant and refugee children have been considered from the lens of social justice research paradigms in terms of the opportunities and outcomes of schooling. This study contributes new knowledge that can be useful for children, educators, policy-makers, and social activists, about the ways in which Canadian media discourses frame children's access to social rights and their experiences of education and migration. This knowledge contributes to the sociology of education, childhood studies, studies in social justice, and refugee and migration studies. Additionally, this study explores opportunities to disrupt conventional explanations for the social and material exclusion of children, in order to advance campaigns for immigration and educational justice.Keywords: Children; Education; Immigration; Media; Migrant Justice; Neoliberalism
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.038 | 0.052 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".