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Record W2979947639 · doi:10.21783/rei.v5i2.389

“WAGGING THE DOG”: FEIGNING CRISIS IN U.S. ANTI-MIGRATION NARRATIVES TO CREATE CRISIS

2019· article· en· W2979947639 on OpenAlex
Maureen Duffy

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueREI - REVISTA ESTUDOS INSTITUCIONAIS · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNarrativeIdeologyPolitical sciencePhenomenonPolitical economyCriminologySociologyLawPoliticsEpistemologyLiteratureArt

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.307
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it