“VILLAIN” VERSUS “VICTIM”: A NEWS HEADLINE ANALYSIS AND LITERATURE REVIEW
Bibliographic record
Abstract
An unprecedented 65.6 million people around the world have been forcibly displaced from their homes; 22.5 million of these individuals are refugees and 10 million are considered stateless (UNHCR, 2016a). Although, the conflict in the Syrian Arab Republic and the large arrival of asylum-seekers at the European borders in 2015 and 2016 gained significant global media attention, the frequently interchangeable use of the terms “migrant” and “refugee” to describe population within news headlines imposes significant implications on the legal protections entitled to each label. Conflating refugees and migrants can undermine public support for refugees and the asylum system at a time when there are more refugees than at any previously recorded time (UNHCR, 2016a). To better understand, two electronic databases CBCA Reference and Current Events and Canadian Periodical Index were searched for headlines containing the key term “refugee(s)” in Canadian English language newspapers. A total of 94 words were identified that offered an indication of the portrayal of refugees in the news media. Fourteen words were categorized as positive, 56 words were negative and 25 words were identified as neutral In addition, a literature review was conducted mirroring the five-stage Arksey and O’Malley (2005) scoping review framework and a data charting form was used to extract descriptive themes. Seven electronic databases (Sociology, Social Sciences Abstract, Web of Science, Communication Source, JSTOR and Social Work) were searched.. A total of 32 studies were selected for the literature review (16 through the database search and 16 through reference list search). From the included literature, five descriptive themes were found: securitization, immigration, legitimacy, economic considerations and othering. Both analyses indicated a dominant securitized discourse that portrays refugees negatively as a threat to the state and revealed a decrease in humanitarian dialogue within news media coverage. Positive representations of refugees were limited to the narrative of a passive victim or contributors to the economy. In addition, refugee voices within news media were restricted to their expression of gratitude towards the state and their compassion. The lack of refugee perspective and the use of journalistic semantic strategies perpetuate a one-sided negative portrayal of refugees. A more balanced media narrative is required to ensure that refugees are being portrayed accurately and their voices are heard.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.061 | 0.055 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".