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Record W2974287534 · doi:10.1080/10439463.2019.1666846

Social media and border security: Twitter use by migration policing agencies

2019· article· en· W2974287534 on OpenAlexaffabout
James P. Walsh

Bibliographic record

VenuePolicing & Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSocial mediaScholarshipPublic relationsEnforcementLaw enforcementPolitical scienceOrder (exchange)Transformational leadershipState (computer science)BusinessLaw

Abstract

fetched live from OpenAlex

Social media are transforming public communication and state-society relations, dynamics distinctly visible in the domains of policing and order maintenance. Despite growing research on this relationship, scholarship has adopted an internalist optic, privileging social media use by domestic law enforcement. Using an original data set, this paper broadens the scale of analysis to consider Twitter usage by federal agencies tasked with border security and migration policing in Australia, Canada, and the United States. Despite new technologies’ transformational potential, its findings suggest Twitter is overwhelmingly employed for the conventional purposes of broadcasting information, managing impressions, and enlisting public assistance. Message themes linked with greater user responsiveness are also identified. The deeper implications of these findings and directions for future research are discussed.

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)0.001
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.372
Teacher spread0.319 · 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

Citations35
Published2019
Admission routes2
Has abstractyes

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