Bridging the gaps between publishing law, academic research and community legal literacy: the potential place of PACLII
Bibliographic record
Abstract
PacLII’s primary focus is improving access to justice by making laws freely available to all. The immense amount of material on PacLII, along with the specialised knowledge required to understand legal material, means that access to the “raw data” of cases and legislation may be of limited help in directly improving non-specialists’ access to justice. Even lawyers rely on tools such as case headnotes and annotated legislation, when available, to help process raw data. Other legal information institutes have begun to implement a number of strategies to help make raw data easier to understand. For example, CanLII Connects (Canada) has used blogs to implement a case summary system (http://canliiconnects.org/en); AustLII Communities (Australia) has used wikis to develop law handbooks (http://austlii.community/foswiki/Main/WebHome); and HKLII (Hong Kong) links with virtual Community Legal Information Centres (http://www.clic.org.hk/en/aboutus.shtml). Most of these initiatives rely on communities of authorised contributors, both academics and practitioners, who freely provide content. All of these are solutions that PacLII could implement, if Pacific countries were interested. There are other roles that PacLII could play as well, including: being a central hosting & coordination point for legal literacy material that is already produced by other agencies; hosting academic research networks; and developing specialised databases. This public seminar will consider possibilities for, and issues with, an expanded role for PacLII in Vanuatu. Other issues with accessibility of PacLII, including access for visually impaired users and access where there is limited internet will also be raised. It is hoped audience discussion will lead to the development of trial activities. All PacLII users who would like to contribute to this discussion are encouraged to attend.
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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".