Bibliographic record
Abstract
How can we leverage digitization to improve access to justice, without compromising the fundamental principles of our legal system? eAccess to Justice describes the many challenges that come with the integration of information and communication technologies into our courtrooms, and explores lessons learned from digitization projects from around the world. \n \nEdited by Jane Bailey and Valerie Steeves. Contributions by Trevor Scott Milford; Akane Kanai; Assumpta Ndengeyingoma; Jacquelyn Burkell; Madelaine Saginur; Priscilla M. Regan; Diana L. Sweet; Jessica Ringrose; Laura Harvey; Jordan Fairbairn; Andrea Slane; Shaheen Shariff; Ashley DeMartini; Gillian Angrove; Matthew Johnson; Sarah Heath; Betsy Rosenblatt; Rebecca Tushnet; and Leslie Regan Shade. \n \nKeywords: Privacy, identity, equality, online environment, women, cyberfeminism, policy
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.826 |
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; both teacher heads 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".