Constructing the refugee: Comparison between newspaper coverage of the Syrian refugee crisis in Canada and the UK
Bibliographic record
Abstract
The media play a key role in informing public opinion during refugee crises. Representations of refugees in the media shape public understanding of what a ‘refugee’ is and policy decisions over who to include or to exclude. Although extensive literature has examined representations of refugees in news media, few systematic comparative investigations look at discourses across types of immigration countries. In this article, the author compares news coverage of the Syrian refugee crisis in Canada and the UK, to consider how media discourse is affected by a nation’s historical relationship with and current policies of immigration. The author follows existing literature in arguing that the dominant discourses in the newspapers racialize refugees through a ‘victim–pariah’ couplet, and further argues that this shared model of racialized representation serves the particular nation-building projects and asylum regimes in the two countries. In addition, a comparison between coverage in newspapers that represent divergent political orientations shows that news stories that attempt to ‘give voice’ to refugees are more prevalent in the more left-leaning newspapers in both countries. Nonetheless, these attempts to ‘re-humanize’ refugees do not invalidate the Orientalist image of refugees as passive victims without agency and history.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".