Bibliographic record
Abstract
While only enrolling fifteen judges at its inception on May 19, 1995, JAIN today emol?s over eight hundred federally appointed Canadian judges and extends to thou sands more provincial court judges by an allied system, JUDICOM (CJC, 1994-2002). Mr. Justice Robert Carr, of Manitoba's Court of Queen's Bench, and chairperson of the JAIN Steering Committee, comments, There is no comparable network in the world. I was at a technology convention in Los Angeles not long ago with about 3,000 delegates, many of them judges, and the Americans just astonished at how far behind they were (Makin, 2000). This management note for students of court administration provides an overview of the operation of JAIN and JUDICOM, assesses their impact on Canadian judges and jus tices, and discusses implications for judicial reasoning. Of course, there are limits in assessing JAIN/JUDICOM due to the lack of systematic understanding through survey
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.014 |
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".