Bibliographic record
Abstract
On September 1, 2020, LLMC, a non-profit Minnesota-based consortium of law libraries, launched the open-access portal RIGHTS! (http://www.llmc.com/rights/home.aspx). If you are looking for primary materials such as current constitutions, human/civil rights acts, Non-Governmental Organizations’ websites, advocacy organizations, and other resources specifically dealing with injustices regarding marginalized parties, this is the place to look. Their stated mission is preserving legal titles and government documents, while making copies inexpensively available digitally through its on-line service, LLMC-Digital (http://www.llmc.com/about.aspx). The original intent was to focus on primarily US and Canadian sources, as seen by the dropdown navigation on the left of the site, but the site also includes other international sources. The page opens at the “Civil and Human Rights Law Portal—Global,” which includes links to various government organizations, judicial information, non-governmental organizations, research and education resources and various documents from different countries. The RIGHTS! site can also be reached through the parent page (http://LLMC.com) with the link to RIGHTS! Located in the right-hand column. The RIGHTS! Portal is sponsored by the Vincent C. Immel Law Library at Saint Louis University.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.735 | 0.626 |
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".