Special Section to Celebrate Twenty Years Of Publishing: Access to Justice And Law Reform # 2
Bibliographic record
Abstract
Some 10 years ago I had the privilege of participating in a Symposium organized by the Windsor Yearbook of Access to Justice. In Part One, I draw out lessons of five different experiences I have had over the past quarter-century toiling in the fields of community legal education and access to civil justice. Nonetheless, public legal education in the service of access to justice is a double-edged sword. Now I have come to believe that traditional public legal education in the service of access to justice may well have too narrow a focus and too narrow a target. One of the central aims of the Programme was to undertake empirical research to test various hypotheses about the reasons why so many Canadians had virtually no access to official law. Yet our processes of official law-making by Parliament are far less accessible for reasons of cost, delay and complexity than even the least accessible judicial forum.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.110 | 0.031 |
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".