Bibliographic record
Abstract
this document. We will look at five of those projects, the ones we felt were especially likely to give us insight into the models and the strategic choices that were made. The projects cover individual courts as well as whole justice systems. The projects we examined are also diverse. They illustrate partnerships with business and initiatives that draw on the strengths of the legal system. Some favour a single service provider, while others focus more on interoperability and open standards. Together, these projects provide a good overview of the solutions currently used by courts to offer electronic filing. The project carried out by the Federal Court of Australia was designed to meet the court's specific needs and is being phased in. The national e-filing project in Singapore, a country well known for its commitment to the use of information technologies, uses a very different approach. It is a national project, but is being developed entirely by the private sector. The system, designed by the administrative services of American federal courts, shows how much the pragmatic approach taken by American federal colleagues has led to success. The only major Canadian project was conducted in the Toronto region and entails experimental use of common formats like MS-Word to exchange information between lawyers and courts. The last project we examined, the E-Court Filing project carried out in Georgia by the Georgia Courts Automation Commission, is something of a window into the most common technological choices being made today. The following summaries were based on the available literature, which in some cases was sparse. For that reason, they are necessarily lacking in detail. The overview they provide of recent e-filing models strikes us as useful none the less. We just hav...
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.522 | 0.260 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".