Assessing Privacy Practices on Ontario Municipal Websites
Bibliographic record
Abstract
The sharing of personal data on government websites is a major concern of daily users. The existing regulations do not allow for the collection and sharing of personal data. This study investigates the privacy practices of 444 municipal websites across Ontario, Canada, focusing on compliance with relevant data protection regulations and the extent of third-party data sharing. In particular, we examine the issues in line with Canadian standards, the Personal Information Protection and Electronic Documents Act (PIPEDA), the Canadian Privacy Act (CPA), and the Municipal Freedom of Information and Protection of Privacy Act (MFIPPA). We perform network traffic analysis, and apply a combination of privacy policies. Our findings uncover substantial gaps in privacy practices, including insufficient transparency, inadequate user-consent mechanisms, and pervasive third-party data sharing. The results of our study highlight an urgent need for the enhancement of privacy measures on government municipal websites to protect the personal data of users and for the implementation of practices that comply with local and international privacy laws, such as PIPEDA, CPA, MFIPPA, and the General Data Protection Regulation (GDPR). The study provides actionable recommendations aimed at strengthening data protection and restoring public trust in digital municipal services.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".