2019-2024 Strategic Plan: Canadian Research Knowledge Network
Bibliographic record
Abstract
Vision The world’s knowledge is accessible by all. Mission CRKN advances interconnected, sustainable access to the world’s research and to Canada’s documentary heritage content. About For our member organizations and the diverse communities they serve, CRKN empowers researchers, educators, and society with greater access to the world’s research and Canada’s preserved documentary heritage, now and for future generations. We deliver value to academic libraries, heritage organizations, and knowledgeseekers within Canada in the following ways: > Represent our membership in large-scale licensing and content acquisition activities; >Collaborate to expand and enrich the digital knowledge ecosystem in Canada and the world; >Advocate for fair and sustainable access to public research and content; >Support the digital infrastructure required to preserve and access critical Canadian content; >Mobilize our membership to transform scholarly communications in Canada. Our Members CRKN members represent 76 academic libraries across Canada that include world-class research institutions, innovative teaching-focused institutions, as well as two national libraries, and Canada’s largest public library system. Our Commitment CRKN is committed to help expand, advance, transform, preserve, and enrich access to knowledge.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.106 | 0.073 |
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".