Wearables and Sur(over)-Veillance, Sous(under)-Veillance, Co(So)-Veillance, and MetaVeillance (Veillance of Veillance) for Health and Well-Being
Bibliographic record
Abstract
At the University of Toronto, we’re embarking on a bold new initiative to bring together these four disciplines: law, business, engineering, and medicine, through what we call “sousveillant systems”—grassroots systems of “bottom up” facilitation of cross-, trans-, inter-, meta-, and anti-disciplinarity, or, more importantly, cross-, trans-, and inter-passionary efforts. Passion is a better master than discipline (to paraphrase Albert Einstein’s “Love is a better master than duty”). Our aim is not to eliminate “big science,” “big data,” and “big watching” (surveillance), but to complement these things with a balancing force. There will still be “ladder climbers,” but we aim to balance these entities and individuals with those who embody the “integrity of authenticity” and to provide a complete picture that is otherwise a half-truth when only the “big” end is present. This generalizes the notion of “open source,” where each instance of a system (e.g., computer operating system) contains or can contain its own seeds (e.g., source code). Sousveillant systems are an alternative to the otherwise sterile world of closed-source, specialist silos that are not auditable by end-users (i.e., are only auditable by authorities from “above”).
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How this classification was reachedexpand
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".