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Record W2905220873 · doi:10.1177/2514848618816991

It's a bird! It's a plane! An aerial biopolitics for a multispecies sky

2018· article· en· W2905220873 on OpenAlexaboutno aff
Charlotte Wrigley

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsBiopowerAviationAgency (philosophy)AeronauticsSociologyPolitical scienceEngineeringLawPoliticsSocial science

Abstract

fetched live from OpenAlex

Bird strikes were catapulted into headline news in 2009 when US Airlines flight 1549's engines ingested a flock of Canada geese and lost all power, leaving the pilot no option but to ditch into the freezing cold Hudson River. Although everyone on board survived, thousands of birds were killed in the years that followed in attempt to redress aviation safety concerns. This article follows the story of Flight 1549 and considers the different stages of bird strike prevention at a variety of sites: the factory, the airfield, the sky and the accident aftermath. Drawn from empirical research and grey literature analysis of aviation safety documents, it unpicks the various assemblages that are formed at each site and how they are gathered together through inhuman air forces. Situated within theories of biopolitics, it moves beyond a materialist analysis of the solid and the visible and attends to the immateriality of air and its elemental properties which are integral to both life and death. Through an analysis of the aerial as a socio-material spatial categorisation, it considers bird strike management within a multispecies perspective by examining the frictions and entanglements of both human and non-human agency that are generated by differential air spaces. By highlighting the different forms of biopolitics produced by the aerial, it shows how configurations of life shift in relation to the dynamic and unpredictable inhuman forces of air, and how aviation safety practices attempt to harness these forces.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.646

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 studies0.0010.001
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 head0.022
GPT teacher head0.318
Teacher spread0.295 · 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 designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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