The Open Library at AU: Supporting Open Access and Open Educational Resources
Bibliographic record
Abstract
In recent years, there has been a shift toward “openness” in higher education, with the growth of Open Access and Open Educational Resources (OERs) and the advent of Massive Open Online Courses (MOOCs). There is also recognition of the unique challenges and opportunities these concepts present to higher learning. Libraries have long played an important role in supporting open access. One of the main drivers of library support for open access is the continuously rising cost of journal and database subscriptions. According to a position statement by the Canadian Association of Research Libraries (CARL), in addition to mitigating these rising costs, open access allows for greater access to research by the public, and “maximize[s] the return on [tax payer and funder]investment in research, advancing discovery and innovation, sound public policy, enhanced health and welfare, and other benefits important for society” (CARL, 2014). These rising costs, coupled with the issues outlined in the CARL position statement point to increased library support and involvement in open access...
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
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 | Not applicable | low |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Commentary 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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.025 |
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".