Framing Fear: Loss Aversion and Availability in Trump’s Immigration Rhetoric
Bibliographic record
Abstract
This paper examines the role of the cognitive biases of the availability heuristic and loss aversion in shaping voter preferences and public support for Donald Trump’s immigration rhetoric and policies. The study, grounded in behavioral economics, examines how loss-framed narratives, such as those of economic and cultural threats posed by immigration, mobilize voter support by leveraging fears of perceived losses. Simultaneously, Trump’s reliance on emotive anecdotes amplifies the salience of isolated events, distorting public perception of immigrants as disproportionately linked to crime and economic strain. Despite empirical evidence highlighting the economic contributions and lower crime rates among immigrant populations, these biases, namely the availability heuristic and loss aversion, drive support for stringent immigration measures, including travel bans and deportations for particular immigrant groups. This paper argues for corrective measures such as embedding anecdotal narratives within public campaigns, policy-making forums, and educational curricula alongside enhancing public data literacy to mitigate these biases in political discourse and voter choices.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".