The Discursive Strategies Used in Representing Refugees in the British News Media: A Critical Discourse Approach
Bibliographic record
Abstract
This study examines the discursive strategies employed in some British newspapers concerning their reports on Ukrainian refugees. While prior research has shown that news media has adversely portrayed non-European refugees, this study is distinct in that its research sample is exclusively European refugees. The basic goal was to show how the mainstream media depicted the Ukrainian immigrants arriving in the UK. The data was gathered from 64 articles about Ukrainian refugees, released in four UK mainstream news outlets between April 2022 and August 2023. The data was analyzed into six analytical categories within the Discoursal Historical Approach (DHA) framework: discourse references, subject-predicate combinations, argumentations, perspectivising discourses, repair strategies (mitigation), and intensification. The study findings showed that all four media outlets, irrespective of their respective ideologies, regularly utilized the discursive techniques of individualization and humanization, thereby establishing a widely accepted and constructive arguing strategy regarding Ukrainian refugees. Prevalent approaches portray Ukrainian refugees in a favorable light. Despite the country's media's generally negative representation of third-world refugees, the results of this study show that the British press purposefully depicted Ukrainian migrants in a positive and sympathetic light. This sets the study's findings apart from those of previous studies. It is believed that the media's ideological stance toward Eurocentrism and warped racial ideas constituted a major role in shaping how European and non-European migrants were portrayed in news reports.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".