The portrayal of refugees in Canadian newspapers: The impact of the arrival of Tamil refugees by sea in 2010
Bibliographic record
Abstract
News media make an essential contribution to the way in which the public processes and understands controversial issues such as the arrival of refugees in western countries. Indeed, they can have an important role in shaping the public’s responses to these issues by framing arguments to encourage a particular interpretation of an issue. The current research investigates how refugees were portrayed before and after the controversial arrival of a ship carrying Tamil refugees to Canada in August 2010. The study was based on the content analysis of 102 articles published in six Canadian newspapers six months before and six months after the event. The newspapers were selected based on their large circulation and diverse political slants. The analyses revealed substantial variation in the extent to which the newspapers reported on the issue of refugee arrivals, as well as in their portrayals of refugees. Liberal newspapers were more likely than conservative newspapers to include reports on issues surrounding refugees and were more likely to portray refugees as victims. Also, the analyses demonstrated the impact of the arrival of the Tamil refugee ship on the portrayal of refugees. Whereas before the event refugees were portrayed more in terms of false claims for refugee status, after the event refugees were portrayed more in terms of being either criminals and terrorists or victims. These results have important implications for how refugees are perceived and treated in society, including what kind of policies are implemented to handle refugee claims and what type of assistance is provided to refugees.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| 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".