Strangers in a strange land: visualizing Syrian refugees in U.S., Canadian, and Lebanese newspapers
Bibliographic record
Abstract
Although news photographs of refugees are often perceived as objective representations of reality, they are actually the product of subjective decisions made by photographers and editors. These subjective realities are reinforced by captions when they are published in newspapers. Using a quantitative content analysis method, this study aims to understand how Syrian refugees were framed visually and lexically in the online editions of national newspapers from the United States, Canada and Lebanon from 1 September 2015 to 31 March 2017. Photographs, accompanying captions and headlines were collected from the New York Times (United States), the Globe and Mail (Canada) and Annahar (Lebanon). Eighteen variables were designed and adapted from previous research to code the sample. The results of this study complement previous research on framing of the current refugee crisis, much of which focuses on European newspapers. This paper provides valuable insight into how representation of Syrian refugees in Anglophonic newspapers compares to that of Lebanese newspapers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".