Bibliographic record
Abstract
Ryerson University Libraryin Toronto, Canada, embarked on an electronic reserve pilot project in 2004. The project soon took on new directions, including integration with Blackboard, using its new Content Management System (January 2005), employment of SFX links to offer database options for accessing articles (April 2005) and experiment with streaming a digitized video (May 2005). The reasons for these innovations are multifold. User's convenience of access via a portal like Blackboard is paramount. The SFX multi-database menu is revolutionizing electronic reserve access, though the concept has yet to be tested widely. Streaming AV content is ideal for distributed learning. From the staff perspective, the OpenURL Connector for SFX links is convenient for locating databases and creating links. SFX links are maintained centrally by the Collections Team in the Library and may reduce the occurrences of broken links. Some technical issues remain to be resolved. Extra library authentication after the Blackboard log-in and multiple clicks for downloading articles through SFX are the major ones reported. Excessive copy right clearance costs for digitized videos, lengthy course packs, business cases and so on are delaying or prohibiting some electronic reserve requests. The project has expanded the Library's collaboration with other e-learning stakeholder groups on campus and heightened copyright awareness in the University community.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.213 | 0.038 |
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".