Could we do better? Behavioural tracking on recommended consumer health websites
Bibliographic record
Abstract
OBJECTIVE: This study examines behavioural tracking practices on consumer health websites, contrasting tracking on sites recommended by information professionals with tracking on sites returned by Google. METHODS: Two lists of consumer health websites were constructed: sites recommended by information professionals and sites returned by Google searches. Sites were divided into three groups according to source (Recommended-Only, Google-Only or both) and type (Government, Not-for-Profit or Commercial). Behavioural tracking practices on each website were documented using a protocol that detected cookies, Web beacons and Flash cookies. The presence and the number of trackers that collect personal information were contrasted across source and type of site; a second set of analyses specifically examined Advertising trackers. RESULTS: Recommended-Only sites show lower levels of tracking - especially tracking by advertisers - than do Google-Only sites or sites found through both sources. Government and Not-for-Profit sites have fewer trackers, particularly from advertisers, than do Commercial sites. CONCLUSIONS: Recommended sites, especially those from Government or Not-for-Profit organisations, present a lower privacy threat than sites returned by Google searches. Nonetheless, most recommended websites include some trackers, and half include at least one Advertising tracker. IMPLICATIONS: To protect patron privacy, information professionals should examine the tracking practices of the websites they recommend.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".