A Corpus-assisted Critical Discourse Analysis of the Representation of Syrian Refugees in Canadian Newspapers
Bibliographic record
Abstract
Humanitarian immigration plays a significant role in building a positive identity for Canada as compassionate and welcoming to those who “deserve” and need protection. Canadian media representations reify this positive self-image and position Canadians in favor of or against refugees; the media’s influence on the public’s perception of immigration has been demonstrated by a significant body of scholarship. Public perception helps determine how Canadians treat newcomers, which, in turn, affects “the precarious resilience of multiculturalism in Canada”. This study critically investigates the representation of Syrian refugees in five Canadian newspapers: the Globe and Mail, the National Post, the Toronto Star, the Toronto Sun, and the Ottawa Citizen. It brings together prominent approaches in Critical Discourse Studies (CDS) and tools in corpus linguistics under the overarching methodological framework of Corpus-assisted Critical Discourse Studies to analyze themes surrounding Syrian refugees and the discursive construction of Syrian refugees and the Canadian government and public in each theme. A total of 340 news articles published between 2015 and 2017 were analyzed. The findings reveal that although the newspapers generally welcomed the resettlement of Syrian refugees and unanimously praised Canadians and their humanitarian response, representation of refugees and the Canadian government varied among newspapers according to political affiliations, coverage, and stylistic features. The findings also indicate that discriminatory discourses such as refugees as political tools, security threats, economic burdens, criminals, and incapable of integration exist in tandem with empowering representations that render refugees as self-sufficient, determined, willing, and well-able to become part of the Canadian community. These findings suggest that there is a capacity in the media for more balanced and empowering representations of refugees. This research adds to existing literature using Corpus-assisted Critical Discourse Studies, which combines quantitative and qualitative methods to offer an analytically balanced approach to the investigation of news media representation of immigration in an era of increasing global displacement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".