The Construction of Refugees in the Canadian News Media: A Critical Discourse Analysis
Bibliographic record
Abstract
Introduction: Immigration, Refugees and Citizenship Canada has estimated that 9,237 refugees arrived in 2020, and 20,391 in 2021. Many of these refugees have been racialized people who have encountered racism during settlement. News media reporting is one such channel of racism. Objective: The aim of this paper is to answer the research question, how are refugees constructed in Canadian news media? Theory: Using a Critical Discourse Analysis methodological approach, we adapted Ruth’s Wodak’s Discourse Historical Approach and Framing Theory of Media Analysis. Methods: We used the ProQuest Canadian Newsstream database to search for articles across six newspapers: National Post, Globe and Mail, Calgary Herald, Calgary Sun, Edmonton Journal, and Edmonton Sun using the search terms “refugee” and “asylum seeker”. We limited our search to articles published between 2011 to 2021. We used Nvivo version 12 to examine, code, and analyze our dataset. Findings: We had a total of 1,618 results across the newspapers. After title and abstract screening, we had 1,293 articles and after screening full texts, 325 newspaper articles. We found three major themes, representing how refugee identity was constructed in Canada from 2011 to 2021. Two of these themes were frames. These were 1) Commodity and 2) Threat. There were two sub-themes under each of these themes. Under Commodity, there were Helpless Victims and Working Bodies and under Threat, there were Terrorist and Criminal. The third major theme was Dehumanization, which as opposed to a resulting frame, was a discursive strategy that underlay and weaved through the refugee identity constructs. Conclusion: Our findings demonstrate that dehumanization is crucial to the construction of refugees in Canadian media as a precursor for humans to commit or support racist, punitive refugee policies. In effect, to dismiss the refugee's physical and psychosocial wellbeing, economic security, safe and healthy living environment, and other needs for survival, one must first dismiss the refugee’s humanity.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.033 | 0.034 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".