Hidden Surveillance on Consumer Health Information Websites
Bibliographic record
Abstract
Behavioural tracking presents a significant privacy risk to Canadians, particularly when their online behaviours reveal sensitive information that could be used to discriminate against them. This concern is particularly relevant in the context of online health information seeking, since searches can reveal details about health conditions and concerns that the individual may wish to keep private. The privacy threats are exacerbated because behavioural tracking mechanisms are large invisible to users, and many are unaware of the strategies and mechanisms available to track online behaviour. In this project, we seek to document the behavioural tracking practices of consumer health websites, and to examine the privacy policy disclosures of these same practices. The results of our research demonstrate that tracking is widespread on consumer health information websites; furthermore, sites recommended by Information Professionals are similar to sites returned in Google searches in terms of overall tracking, though they show lower levels of third-party advertiser presence. Privacy policy disclosure of tracking practices is largely ineffective, and website visitors cannot easily determine tracking practices from a review of the website privacy policies. Taken together, these results suggest that alternative mechanisms are required to detect and/or mitigate or neutralize the behavioural tracking measures used on many consumer health information websites.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".