Threatened or threatening? The framing of asylum seekers from the United States in the Canadian newsprint media
Bibliographic record
Abstract
Following the implementation of Donald Trump’s Executive Order Protecting the Nation from Foreign Terrorist Entry into the United States, Canada has seen an increase in asylum seekers irregularly entering the country from the United States. The Canada-US Safe Third Country Agreement is viewed as the main factor why asylum seekers have been crossing irregularly rather than at official border crossings. This study examines how the Canadian newsprint media has been framing these asylum seekers by analyzing 83 articles published in the National Post and The Globe and Mail between January 27, 2017 and April 27, 2017. A directed content analysis and social constructionist lens revealed seven dominant framings of asylum seekers, with the ‘victim/human rights’ framing occurring most frequently. The results of this study show that asylum seekers are more frequently being framed positively than negatively, a likely result of Canadian attempts at national self-differentiation from a negatively-perceived America.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".