Balancing Privacy and the Open Court Principle of Family Law: Does De-Identifying Case Law Protect Anonymity?
Bibliographic record
Abstract
This paper discusses the right to privacy in the context of family law cases. In balancing the open court principle against the right of the individual to remain anonymous, it is argued that there is a greater public good in maintaining transparency of the judicial process. Disclosing the personal details of individuals who use the public court system allows the public to hold the judiciary accountable for their decisions. While advocates for greater privacy would argue that the personal details of litigants have no bearing on the legal rules that emerge from cases, landmark decisions such as Murdoch v Murdoch and Pettkus v Becker demonstrate how these same details colour the facts and allow for greater empathy and public activism. This is especially relevant where citizens feel that the courts have made a wrong decision. While preserving anonymity is important to protect vulnerable parties such as children, it is difficult from a technical standpoint to maintain anonymity for those involved in public proceedings. The widespread availability of personal information online, coupled with the expansion of online case law databases, facilitates the identification of individual litigants, even if case law is anonymized. Anonymizing family law cases by default is therefore a moot exercise that should not expand beyond the need to protect vulnerable individuals.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".