Can Browser Add-Ons Protect Your Children from Online Tracking?
Bibliographic record
Abstract
Online tracking of children by third-parties is strictly regulated by law in many regions of the world (e.g., COPPA in USA and GDPR in EU), and in a large number of situations constitutes criminal activity. Unfortunately, the existence of these laws does not seem to be an effective deterrence. In this paper, we provide a brief summary of our findings pertaining to the effectiveness of four popular browser add-ons in protecting against third-party tracking on a select number of children-oriented Web-sites. The obtain results show that protection from tracking by a browser add-on is generally achieved at the expense of Web-page performance. In other words, add-ons that are effective at blocking third-party trackers will often adversely affect the normal functioning of the visited Web-page(s). In addition, our results also show that when it comes to user/children tracking by well-known 'tech giants', all four add-ons are likely to provide only limited protection.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".