It's a bird! It's a plane! An aerial biopolitics for a multispecies sky
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".