Puzzling Through the Pieces Together: Developing the Preservation Service Model for a National Repository Service
Bibliographic record
Abstract
Institutional repositories (IRs) house a significant record of unique academic works. Preserving these materials is vital to ensure ongoing access and usability, however there are several obstacles for preservation in the IR context. This paper outlines how the preservation puzzle is being approached by Scholaris, a new Canadian shared IR service that aims to support the open discovery, management, sharing, and preservation of Canadian scholarship. The service is being developed through an Early Adopter Program coordinated by Scholars Portal at the University of Toronto Libraries and expert groups facilitated by the Canadian Association of Research Libraries. This includes the Scholaris Digital Preservation Expert Group, which is lending support to create the digital preservation offering for the service. Beginning with an overview of digital preservation in the IR context and the Canadian landscape that led to the formation of Scholaris, the authors outline the goals and ongoing work of the expert group and reflect on challenges and lessons learned so far.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".