"Trojan Horse" vs. "New Canadians" - News Discourses on Refugees in the United States and Canada after the 2015 Paris Attacks
Bibliographic record
Abstract
Following the 2015 Paris attacks, public opinion on refugees drastically diverged in Canada and the United States. Whereas Canadians became more supportive towards taking in Syrian refugees, US-Americans increasingly disapproved the intake of refugees. In the weeks after the attacks, the fact that one of the attackers entered Europe with a Syrian passport through the refugee roads initiated a global discussion on the safety of taking in Syrian refugees. Especially television news were of major importance in pushing this debate. Through critical discourse analysis, this bachelor thesis examined whether the divergent trends in public opinion in the United States and Canada could be explained by differing news discourses. The analysis found that the US discourse extensively focused on refugees as a potential threat to national security. Due to the fact the US had entered the pre-election phase at this point, the discussion on refugees was markedly politicized. Especially Republican presidential candidates were given plenty of airtime to comment on refugees, while refugees themselves were left almost entirely voiceless. In contrast, the Canadian news discourse discussed the crisis more from a humanitarian perspective, extensively covering philanthropist Canadians who were aiding Syrian refugees through different means such as privately-sponsoring, providing medical care or donating money, clothes and housing. This analysis demonstrates that the news discourses in Canada and the US were indeed profoundly different. This supports the theory that different reporting on refugees in the wake of the Paris attacks contributed to the shifts in public opinion in Canada and the US.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".