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Record W2529693950 · doi:10.1177/0263775816670653

Sky watching: Vertical surveillance in civil aviation

2016· article· en· W2529693950 on OpenAlexafffund
Weiqiang Lin

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNational University of SingaporeCommonwealth Scholarship CommissionRoyal Geographical Society
KeywordsSkyCivil aviationAviationVisionPolitical scienceComputer securityComputer scienceSociologyEngineeringGeographyMeteorology

Abstract

fetched live from OpenAlex

Scholars have written extensively about vertical surveillance as an extension of aeriality. They have interrogated the aerial platform as a synoptic means to behold, control and wage war from above, and as a locus through which air power can be challenged from below. While this perspective highlights the complex reciprocities linking the sky and the earth, its focus remains fixated on aeriality’s (in)capacities to render terrestrial life explicit and governable. As an inhabitable space, the sky is rarely considered as a target of rational knowing and control through vertical surveillance. This article examines the views generated in sky watching, or the tactical monitoring of airspaces in air traffic management (ATM), as a rejoinder. It examines ATM’s methods of knowing the sky, its conservative logics in ‘spacing’ aircraft, and the assembling processes that geopolitically produce unequal surveillant orders. It argues that the sky, along with its visualisations, finds substance through particular technologies, calculations and expertise that repeatedly draw on the West’s visual rationalities. While a ‘benign’ form of seeing/knowing in civil contexts, sky watching in ATM summons geopolitical power not through brute force, but by discerning which visions are ‘acceptable’ for the administration of aviation safety and airborne life.

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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2016
Admission routes2
Has abstractyes

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