Ethical considerations of public health surveillance in the age of the internet of things technologies: A perspective
Bibliographic record
Abstract
In the age of the Internet of Things (IoT), ethical considerations of digitally-led public health surveillance are crucial. However, their application becomes complicated due to an implicit dichotomy of ethical and legal factors. Decision-makers often omit ethical considerations, citing legal ones to justify how public health surveillance is approached and implemented. We propose an analytical framework informed by a further exposition of how influence and power are enacted at the macro, meso, and micro levelscorrelated to a spectrum of ethical practices. We then apply the spectrum of ethical practices to the four use cases of "Healthcare Delivery Using Drones", "COVID-19 Pandemic in Canada", "Air Quality and Air Pollution", and "Heatwaves". When technology deployment prioritizes efficiency over accessibility, it can exacerbate disparities, especially for individuals with lower socioeconomic status and literacy levels. To mitigate these issues, it is essential to incorporate public deliberation, co-design, and community engagement into decision-making processes. This approach advances the incorporation of diverse perspectives to better frame technology initiatives.
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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 | 0.051 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".