#WelcomeRefugees: A Critical Discourse Analysis of the Refugee Resettlement Initiative in Canadian News
Bibliographic record
Abstract
This study focuses on the frames utilized in the depiction of Syrian refugees and social and political actors involved in the Syrian resettlement in Canadian online news media. The role of the media is vital in portraying Syrian refugees' image and affects how the Canadian public perceives them. This paper focuses on utilizing the referential and predicational strategies introduced by the Discourse-Historical Approach (DHA) in framing the Syrian refugees, Liberal government, Conservative party, Canadians, and Canada (henceforth social and political actors). This study examines a total of 31 articles selected from three of the most visited Canadian news sites, namely, the Toronto Star, the Toronto Sun, and the National Post. News articles were collected beginning from the arrival of the first group of refugees in December 2015 and ending in March 2017, which marked the first anniversary of the refugees’ arrival. The results obtained show that both liberal and conservative-leaning media utilized frames in ways that correspond with their ideological stance. In most cases, the limelight rarely focused on Syrian refugees. Instead, they were used as props to push the news source's ideological convictions and to condemn and shame the opposition. Therefore, it is understood, that the framing and portrayal of refugees in this narrow manner through discursive strategies obscures the complexity of the plight of Syrian refugees and depicts them as one-dimensional characters that audiences would either fear or pity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.037 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".