Th Eye Inside: Remote Biosensing Technologies in Healthcare and the Law
Bibliographic record
Abstract
This article focuses on the potential legal impacts of healthcare technologies that can record and remotely transmit biometric data, what the author calls Remote Biosensing Technologies. The legal relevance of remotely recorded biometric data is explained by examining how it can be used in three legal contexts: personal injury cases, search warrants, and informed consent. Within each of these contexts, the author draws from Canadian case law to show how existing legal principles can be modified when dealing with Remote Biosensing Technologies to protect privacy and autonomy while also maximizing the legal utility of the resulting data. The author concludes with recommendations aimed at achieving the immense medical potential made possible by Remote Biosensing Technologies while avoiding the potential harms to individual autonomy posed by the application of the legal gaze.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".