Bibliographic record
Abstract
This presentation explores the status of open access policy\ndevelopments internationally, and particularly in Canada, as of April\n2007. While open access resources are substantial, and growing\nrapidly, the primary issue for open access archives (institutional\nrepositories) is content acquisition, and few researchers fully\nunderstand open access, illustrating an ongoing need for policy. Open\naccess policy initiatives are happening around the world. Sherpa\nJuliet lists more than 20 funding agency policies, from at least 10\ncountries. More than half the policies are by medical research\nfunders. ROARMAP lists at least 40 institutional policies from at\nleast 12 countries. Many more policy initiatives are in the works,\nsuch as the European Commission and the U.S. Federal Research Public\nAccess Act. In Canada, the Social Sciences and Humanities Research\nCouncil adopted open access in principle in 2004, and recently\ninitiated an Aid to Open Access Journals program, a one-year bridge\nprogram for SSHRC subsidized journals. Genome Canada has a strong\nopen access policy for both published research results and data.\nPolicy development is underway at the Canadian Institutes for Health\nResearch, the International Development Research Centre, and the\nCanadian Breast Cancer Research Alliance.
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 | 0.035 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.123 | 0.057 |
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".