Privacy policy disclosures of behavioural tracking on consumer health Websites
Bibliographic record
Abstract
Abstract Many Internet users are seeking health information online, encountering significant privacy risks in the process. Historically, these risks are associated with personally identifiable information, but behavioural tracking presents a new and increasing threat to privacy. In this paper, we analyze the disclosure, in a set of website privacy policies, of the collection of non‐personally identifiable information by consumer health information websites. The websites all engage in first and third party behavioural tracking using cookies and web beacons, and are among the sites recommended by consumer health sections of the Medical Library Association or the Canadian Health Libraries Association (see Burkell and Fortier, , ). Our analysis reveals that while the majority of these sites disclose both first party (6/7) and third party (5/7) behavioural tracking, the language used in these disclosures is difficult to understand, tending to minimize behavioural tracking and obfuscate agency in the tracking process. These results suggest that consumer health information website privacy policies do not provide optimal disclosure of behavioural tracking practices. Library and information science professionals should work with users to ensure they are aware of the behavioural tracking practices of the websites they visit, assisting them to interpret the disclosures provided in website privacy policies.
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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 | 0.015 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".