Promoting Transparency While Protecting Privacy in Open Government in Canada
Bibliographic record
Abstract
The rise of big data analytics, combined with a movement at all levels of government in Canada towards open data and the proactive disclosure of government information, create a context in which privacy issues are increasingly likely to conflict with the goals of transparency and accountability. No new legislative frameworks guide the move towards open government in Canada, notwithstanding the fact that government data is fuel for the engines of big data. This paper considers the challenges inherent in the release of government data and information within this context. Although the recent Supreme Court of Canada decision in Ministry of Community Safety and Correctional Services v Information and Privacy Commissioner (Ontario) (Ministry of Community Safety) did not specifically address either open data or proactive disclosure, this case offers important insights into the gaps in both legislation and case law in this area. This paper assesses how the goals of transparency and the protection of privacy are balanced in Canada in light of the Court’s decision in Ministry of Community Safety. In particular, it considers how “personal information” is to be understood in the public sector context; how courts and adjudicators understand transparency in the face of competing claims to privacy; and how best to strike the balance between privacy and transparency. It challenges the simple equation of the release of information with transparency and argues that the coincidence of open government with big data requires new approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".