Bibliographic record
Abstract
This chapter builds upon the concept of Uberveillance introduced in the seminal research of M. G. Michael and Katina Michael in 2006. It begins with an overview of sousveillance (underwatching) technologies and examines the “We're watching you but you can't watch us” hypocrisy associated with the rise of surveillance (overwatching). Surveillance cameras are often installed in places that have “NO CAMERAS” and “NO CELLPHONES IN STORE, PLEASE!” signage. The author considers the chilling effect of this veillance hypocrisy on LifeGlogging, wearable computing, “Sixth Sense,” AR Glass, and the Digital Eye Glass vision aid. If surveillance gives rise to hypocrisy, then to what does its inverse, sousveillance (wearable cameras, AR Glass, etc.), give rise? The opposite (antonym) of hypocrisy is integrity. How might we resolve the conflict-of-interest that arises in situations where, for example, police surveillance cameras capture the only record of wrongdoing by the police? Is sousveillance the answer or will centralized dataveillance merely turn sousveillance into a corruptible uberveillance authority?
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.089 |
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".