A Cross-National and Cross-Document Analysis of Media and Policy Framing of Education for Displaced Persons in Canada and the United States
Bibliographic record
Abstract
In the face of economic, environmental, and security crises, forced displacement is increasing globally, drawing millions of men, women, and children from their homes and placing them into often unpredictable and indefinite states of transition. For these people, education can provide the knowledge and skills needed to resettle in another country, as well as provide a sense of stability and empowerment in an otherwise difficult context. Since displaced populations (DP) rely heavily on the resources of host countries for the provision of education services, the way that media and policy actors frame and label displaced groups may impact these groups' ability to access quality education. The descriptive mixed-methods analysis in the current study provides an overview of how frames and labels are used in media and policy documents related to the education of displaced populations in Canada and the United States. Examining articles from four national newspapers (n=146) and federal legislation from the U.S. Congress (n=67) published between 2013 and 2022, this study uses Goffman's (1974) Framing Analysis to compare how frames and labels are employed across the sample and how they are used to develop overarching neoliberal, human rights, and threat frameworks. The results of the current study show notable similarities and differences in the use of framing and labeling between Canadian and U.S. media and between U.S. media and legislation. Considering the overall use of framing observed in the sample, this study also addresses the way overarching neoliberal and human rights frameworks compete with one another while overarching threat frameworks can fragment a document's overall framing. This study adds to the body of literature on the framing of DP by placing this discussion in the North American context and by providing qualitative analysis to a set of literature that is mostly composed of quantitative studies.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".