How the Media Frames the Immigration Debate: The Critical Role of Location and Politics
Bibliographic record
Abstract
The media plays an important role in how the American public understands controversial social and political issues, such as immigration. The purpose of this article is to examine how key features of the media, such as location (Arizona vs. National) and political ideology (Liberal vs. Conservative), affect the framing of arguments supporting and opposing the anti‐immigration bill (Arizona SB 1070). A content analysis was conducted using 3 weeks of newspaper articles from two Arizona newspapers (one Conservative, one Liberal) and five national newspapers (three Conservative, two Liberal). Analyses revealed that both location and political ideology influenced the framing. Specifically, the national newspapers were more likely than Arizona newspapers to frame arguments supporting the bill in terms of threats (e.g., threats to economic and public safety) and to frame arguments against the bill in terms of civil rights issues (e.g., racial profiling). In terms of political ideology, Conservative newspapers were more likely than Liberal newspapers to frame the bill in terms of economic and public safety threats, but did not differ in mentions of civil rights issues. The implications for attitudes toward immigrants and legal ethnic minorities and for defining the boundaries of the American national identity 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.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".