In a paperless world a new role for academic libraries: providing open access
Bibliographic record
Abstract
ABSTRACT Academic libraries should be considered research tools, co‐evolving with technology. The Internet has changed the way science is communicated and hence also the role of libraries. It has made it possible for researchers to provide open access (OA) (i.e. toll‐free, full‐text, online access, web‐wide) to their peer‐reviewed journal articles in two different ways: (i) by publishing in them in OA journals, and (ii) by publishing them in non‐OA journals but also self‐archiving them in their institutional OA archives. Librarians are researchers' best allies in both of these strategies. Examples of these strategies are described. We conclude that an official mandate for OA provision is necessary to accelerate its growth and thereby the growth of research usage and impact worldwide.
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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: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.059 | 0.048 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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.
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".