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Record W4396846296 · doi:10.1093/jrs/feae033

Disciplining subjectivity in Australian migrant deterrence campaigns

2024· article· en· W4396846296 on OpenAlexaff
Helena Zeweri

Bibliographic record

VenueJournal of Refugee Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubjectivityDeterrence (psychology)Political scienceSociologyDeterrence theoryGender studiesCriminologyLawEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article examines public information campaigns designed to deter asylum seekers from entering Australia via boat. Through analysing the institutional context and content of a graphic novel that was circulated within Afghan Hazara communities in 2014, I show that certain Australian public information campaigns mobilize an ethos of cultural sensitivity rooted in ethnographic data-gathering projects that reinscribe migrants as ignorant and socially deviant subjects. Such campaigns both situate Australia as an impossible destination and render migration a dangerous, futile act that will bring further misfortune to migrants’ families. The Australian case shows that in contexts where cultural sensitivity and externalized border control simultaneously guide migration policy, cultural knowledge becomes weaponized not only to keep migrants immobile but also to discipline migrant subjectivity and ultimately exclude them from pathways to refuge.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0110.024
Scholarly communication0.0110.004
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.065
GPT teacher head0.420
Teacher spread0.355 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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