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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".