Bibliographic record
Abstract
The lack of access to justice in Ontario is an issue that has wide-reaching consequences. Law librarians can play a role in addressing existing barriers to access. This paper discusses how law librarians can positively contribute to this issue of access to justice and begins with a review of the existing barriers facing the public in pursuing legal research. Following this, it discusses how librarians can uplift the public’s right to information as it relates to both professional and information ethics, library neutrality, and the access to justice movement. Lastly, the paper discusses public libraries as a field with the potential to service the access to justice movement by providing support such as public training and free access to legal information. It challenges law librarians to consider serving the public outside of their organizations’ mandates with both passive and active information-sharing ideas, followed by examples of successful collaboration between law librarians and the public from Law Librarians of New England (LLNE) and the Law Society of Saskatchewan.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.082 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".