Canada's Courts Online: Privacy, Public Access and Electronic Court Records
Bibliographic record
Abstract
Finding the delicate balance between access to public records and personal privacy has been characterized as “one of the most challenging public policy issues of our time.” Court records are a subset of the larger category of “public registries, which can be defined as lists of personal information that are under the control of a public body, maintained by rule, statute or practice, and open in whole or part to public inspection, copying, or distribution. Court records have several special characteristics that set them apart within this larger category of public registries and make finding the appropriate balance between access and privacy especially difficult. his article examines online court records and the fragile balance between the public interest in public access to the courts and the equally public interest in privacy. The article argues that privacy and public access to court records are not mutually exclusive objectives because privacy is not simply a personal interest limited to the individual subjects whose information is vulnerable to exposure more widely and more easily than was contemplated before the introduction of the Internet. If public access, use, and dissemination of court records does not consider both privacy and access, the effect may be to lessen access to justice overall. Given that private information is contained in public records, full access to justice is attained not by full access to information but by a balanced treatment which links access to proper public functions. In collecting personal information, public institutions have a duty to safeguard the privacy of individuals to the extent that is consistent with the goals of transparency and monitoring: there should be access coupled with respect for personal information. More sophisticated technology will facilitate this balance. Because the legislative framework relies on “public purposes” and “publicly available” to define protections for personal information in public records, this paper argues that the conundrum of how to balance privacy and access is best approached by first linking “public records” and the information within those records to the public purposes for the records. By so doing, the analysis will be on the underlying policy objectives instead of the questions of “paper versus electronic” sources or “on-site versus remote” access. The article argues that these practices can limit the amount of personal information that is included in court records while allowing public access to the personal information that is required for the public purpose of monitoring the courts.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".