Bibliographic record
Abstract
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".