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Record W1999396824 · doi:10.1108/14779960380000222

Airports as data filters: Converging surveillance systems after September 11th

2003· article· en· W1999396824 on OpenAlex

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

VenueJournal of Information Communication and Ethics in Society · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsQueen's University
Fundersnot available
KeywordsRefugeeSeekersPoliticsScale (ratio)BusinessAir travelLock (firearm)AdvertisingPolitical scienceGeographyAviationEngineeringLawArchaeologyCartography

Abstract

fetched live from OpenAlex

Airports are crucial channels of mobility for the global citizens of the twenty‐first century. They are points of entry and exit for tourists, business persons, workers, students and of course, for some refugees as well. The scale of operations is huge ‐ international passenger travel increased twelve‐fold in the second half of the twentieth century (Urry, 2000: 50) and the vast majority of this is accounted for in air travel. In the USA alone there are two million daily airtravelers on 20,000 flights (Gottdiener,2001: 1). Airports are ‘placeless’ sites of temporary sojourn, air‐lock chambers for nomadic executives or sun‐seekers. But they have profound social and political significance, particularly in personal data handling.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.848
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.364
Teacher spread0.311 · 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