PLIRC Database: A New Tool for PLAR Scholars, Practitioners and Policymakers
Bibliographic record
Abstract
The Prior Learning International Research Centre (PLIRC), based at Thompson Rivers University - Open Learning in Kamloops, British Columbia, has entered into a partnership with the Canadian Institute for Recognizing Learning (CIRL) and SUNY Empire State College. The partnership is creating a single, PLAR-focused, full-text database to provide easy access to research studies and act as a link between researchers, public policymakers and practitioners. The three partners hope that this new resource will encourage more and better research and collaboration across jurisdictions and disciplines. What follows is a fuller description of this project. In future issues, PLAIO will feature articles from this developing database.
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 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.096 |
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".