When Does the Public Care About Immigration? The Political Salience and Valence of Immigration in Colombia
Bibliographic record
Abstract
What triggers public concern about immigration? Although substantial research has investigated public attitudes toward immigration, less work has been done on its political salience. This study utilizes survey experiments with Colombians to investigate the drivers of both valence and salience concerning Venezuelan immigration. Employing experimental vignettes, the study explores the effects of different styles of rhetorical framing, specifically contrasting moderate anti-immigration framing with strong anti-immigration rhetoric, on attitudes about the salience and valence of immigration. First, we find that rhetoric that leads to more negative (positive) views on immigration also heightens (lessens) its perceived importance, suggesting a previously unacknowledged challenge for mobilizing political support for immigration. Second, strong anti-immigration messaging, akin to the style of rhetoric used by many contemporary populists, is highly effective in influencing opinions. Alarmingly, this rhetoric has broad effectiveness, even among people who did not hold negative views of immigration at baseline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".