Learning As We Go, Go, Go: Reflections from Developing and Delivering an Early Adopter Program for a Shared Repository Service in Canada
Bibliographic record
Abstract
Scholaris is a new national opt-in shared repository service that aims to support open discovery, management, sharing and preservation of Canadian scholarship by providing scalable infrastructure, technical expertise and community support for Canadian institutional repositories. The service is being developed by the Canadian Association of Research Libraries (CARL), the Ontario Council of University Libraries (OCUL) and the University of Toronto Libraries (UTL), in collaboration with regional consortia and the broader repository community. The shared technical infrastructure, built on the DSpace platform, is hosted and managed by Scholars Portal at UTL. In the Spring of 2024, we launched an Early Adopter Program to work with institutions representing a wide range of repository and migration scenarios and through that process, better understand what’s needed to support Canadian IRs from a service perspective. From previous feasibility studies, we knew there was interest in a shared repository service but the program uptake far exceeded our expectations. Over the last year and a half, we’ve onboarded, migrated, and launched more than twenty institutions (!) —and we’ve learned a lot along the way. In this presentation, we’ll share how we’ve project managed a myriad of migrations, what we’ve learned from working closely with our wonderful Early Adopters and Network of Expert Groups, and how these insights are informing the on-going evolution and delivery of the service and the development of community resources and recommendations.
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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.066 | 0.033 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".