Why do They Need to Know I Spotted a Pothole? Privacy Issues in Canadian Municipal Problem Reporting Websites
Bibliographic record
Abstract
Users act as valuable ``citizen sensors'' by reporting issues in public spaces enabling cities to address infrastructure problems in a timely manner. Municipal web sites are one method for collecting such reports, and they often ask for personal information such as name, phone number, and address in addition to reports. Together with common practices such as third party connections and the use of cookies, there is a serious potential for privacy loss, which should be addressed via a clear privacy policy. We examined 14 Canadian Municipal Problem reporting web sites considering issues like what information is required to report, third party connections, and privacy policies. We checked third party connections by sampling the web traffic sent when reporting a pot hole. One city did not provide a privacy policy. The coverage of the remaining 13 varied substantially. For example, five out of 14 cities required personal information to submit a report, but only one had a privacy policy that comprehensively addresses what happens to that data. All city websites contact more third parties during pothole reporting than are covered in their privacy policy. Such clear gaps in privacy policies might negatively affect citizens' trust in the platforms.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".