The Judicial System in the Digital Age: Revisiting the Relationship between Privacy and Accessibility in the Cyber Context
Bibliographic record
Abstract
Despite technology’s reach into all parts of social life, its effects on the judiciary have been under-theorized. The “Digital Age”, and unfettered usage and access to digital information, will have untold effects on core values of judicial independence, impartiality and the delicate balance between privacy and the “open court” principle. Technology—as well as the dramatically increased availability of information of all kinds and quality—is distorting the judicial process and its outcomes. It is of primary importance, therefore, to identify the broad issues that emerge from the growing use of technology, and to provide a theoretical basis for adjudicating the ongoing tension between privacy and transparency in the judicial setting. Too often the judiciary pits privacy against the “open court” principle and accepts a culturally narrow view of what constitutes privacy and how it affects the judicial process. In particular, this article investigates the effects of online court documents to establish why, despite the current preference for openness and transparency, a contextualized understanding of privacy is desirable. Indeed, if we rethink privacy within the cyber context, it can be considered an ally of openness in the court system.
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 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".