“WAGGING THE DOG”: FEIGNING CRISIS IN U.S. ANTI-MIGRATION NARRATIVES TO CREATE CRISIS
Bibliographic record
Abstract
Anti-migration narratives are sweeping around the world, often accompanied by support for racist ideologies. The narratives usually involve some false claim that those seeking to enter the country are presumptively dangerous. Such narratives are obviously not new, but they are arguably being presented in evolving ways and having evolving, and deeply troubling, practical and legal effects. In the U.S., migrants being held in horrific “camp” conditions represent just the latest in a series of anti-migrant measures, each arguably worse than the last. This phenomenon is not limited to the U.S., but that example provides a strong vehicle for demonstrating this larger transnational trend. This article argues that harmful anti-migrant narratives are having significant, adverse effects on human rights and foundational legal norms.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".