Open Access: Accessibility for the Ivory Tower
Bibliographic record
Abstract
Open Access (OA) is a growing international movement that encourages the unrestricted sharing of academic research to benefit all communities. OA is the principle that all research should be freely accessible online, immediately after publication, and it's gaining global momentum with the support of funding agencies and policy makers. OA serves communities, supports innovation and development, makes publicly funded research available to taxpayers, and provides essential information to low income populations. \n \nLibrarians have played an important role in the OA movement. Learn how Memorial University of Newfoundland is connecting communities with knowledge by disseminating research through several OA initiatives, and learn how to make your own research more accessible. \n \nLisa Goddard, Memorial University's Scholarly Communications Librarian, will provide information about its research repository, ejournal hosting platform, and OA author's fund. Humanities Research Librarian, Kathryn Rose, will share her experience of collaborating with students and faculty to start an OA ejournal, and provide tips for attendees interested in creating an OA journal of their own. Copyright Liaison Librarian, Crystal Rose, will discuss the benefits of OA for communities, what university and government policy makers worldwide are doing to support OA mandates (and how Canada is lagging behind), how publishers have responded to the OA movement, and what you can do to advocate for OA within your institution or organization.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communicationOpen science Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.241 | 0.112 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".