Veillance flux, vixels, veillons: An information-bearing extramissive formulation of sensing, to measure surveillance and sousveillance
Bibliographic record
Abstract
The word “surveillance” comes from the French word “veillance” which means “watching” and the French prefix “sur”, which means “from above”. Thus “surveillance” means “to watch from above” (e.g. guards watching over prisoners or police watching over a city through a city-wide surveillance camera network). The closest purely English word is “oversight”. A more recent phenomenon, sousveillance (“undersight”) refers to the less hierarchical and more rhizomic veillance of social networking, distributed cloud-based computing, and body-worn technologies. Sousveillance forms a reciprocal power balance with surveillance, both being understood in the context of not just technology, but also complex human social and political relationships. In this paper we derive a precise theoretical and mathematical framework to understand, interpret, quantify, and classify “veillance” (“watching”) as to its directionality (i.e. surveillance versus sousveillance). While veillance can occur in a variety of sensory modalities, such as auditory sur/sousveillance, dataveillance, etc., we will focus especially on optical (visual) veillance. We define new physical concepts: the veillon, the vixel, and the veillance vector field, to provide insight into the measurement and demarcation of surveillance and sousveillance and their interplay.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".