Veillance flux, vixels, veillons: An information-bearing extramissive formulation of sensing, to measure surveillance and sousveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it