Framing Syrian refugees: US local news and the politics of immigration
Bibliographic record
Abstract
The article investigates news coverage and media framing of the Syrian refugee debate as a public opinion issue in US local news in 2015. The sheer number of refugees created an unprecedented humanitarian crisis as millions of civilians settled in neighboring countries, and a significant number of them embarked on a perilous journey to seek refuge in European countries. The political response to the Syrian refugee crisis was divided, but public attitudes shifted after the terrorist attacks on Paris in November 2015 with calls for more restrictive immigration policies and smaller refugee quotas. In the US, GOP leaders demanded “extreme vetting” and “screening” of refugees and many opposed resettling the modest number of refugees the Obama administration promised to let in. The study analyzes local news coverage variation across the States that welcomed, not welcomed or did not commit to accepting Syrian refugees at the height of the Syrian refugee crisis in 2015 and 2016. The findings of the study demonstrate that the editorial framing of the Syrian refugee crisis downplayed the global responsibility and international commitment of the US, highlighted the administrative costs and framed them as security threats. The implications of these frames are discussed.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".