Reaching the diverse public of Alberta, Canada: How law librarians helped fill a gap in legal information delivery and access to justice through an innovative and collaborative new website
Bibliographic record
Abstract
LegalAve is a new public legal information website in Alberta, Canada. The project was initiated by public and law librarians to help deal with the gap in legal information delivery and access to justice in Alberta. The founding librarians recognized that while much legal information can be found freely on the internet, this increased access can mean trouble for many members of the public who are not discerning consumers of legal information. Information overload, confusion over jurisdiction, and a disconnect between substantive law and the legal process are continual concerns for law librarians committed to making “access to justice” a reality for their users. Similarly, geographical hurdles, language barriers, funding challenges, and navigating the fine line between legal information and advice affect law librarians’ ability to deliver legal information to Albertans. Faced with these challenges, the founding librarians determined that, rather than trying to bring Albertans to the legal information, law libraries should instead work with other experts in the field, and with the reigning paradigms and inescapable hurdles, to bring legal information (and the law library) to Albertans. Thus was born LegalAve. Through LegalAve, librarians and other legal information providers reach the public indirectly, contributing their resources and information to its non-threatening online environment. LegalAve’s “Guided Pathway” feature helps users find the information that fits their situation based on question-and-answer “decision trees.” The website makes the connections between legal information, community services, legal processes, and the basics of legal research (including referrals to law libraries for further assistance). In short: LegalAve is a success story about how Alberta law librarians brought together the strengths of all members of the legal community, and thereby expanded their role to meet the needs of users while remaining a key player in facilitating access to justice.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.086 | 0.019 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".