Framing Syrian refugees: US local news and the politics of immigration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it