Bibliographic record
Abstract
Abstract | This short experimental essay reflects upon our video Points of Presence. In producing the video we used unmanned aerial drones to visually and vertically examine undersea fibre-optic cables of the North Atlantic. We reflect upon how the drone’s flying technologies allow pilots to creatively engage with the atmospheric element. We argue that the drone’s optical and object-avoidance technologies share similarities with the mammalian senses. In concluding, we examine how drones and information infrastructures reflect each other as complex and imperfect systems designed to extend the human body and senses across geographies.Résumé | Ce court essai expérimental se penche sur notre vidéo Points of Presence. En produisant la vidéo, nous avons utilisé des véhicules aériens sans pilote pour examiner visuellement et verticalement les câbles de fibres optiques sous-marins de l’Atlantique Nord. Nous réfléchissons à la façon dont les technologies de navigation du drone permettent aux pilotes d’interagir de manière créative avec l’aspect atmosphérique. Nous soutenons que les technologies optiques et d’évitement des objets du drone partagent des similitudes avec les sens des mammifères. En conclusion, nous examinons comment les drones et les infrastructures d’information se reflètent comme des systèmes complexes et imparfaits conçus pour prolonger le corps humain et les sens à l’échelle de la planète.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".