Future Proofing for IRs: A community-informed approach to preservation planning for Canadian scholarship
Bibliographic record
Abstract
Institutional repositories (IRs) house unique content created by academic communities and hold a significant record of scholarship over time. While preservation of this content is vitally important, it often presents numerous challenges and specific considerations, including developing policies and procedures, building technical workflows, accommodating content with diverse file formats, and managing costs. As such, preservation in the IR context can be a complex, intimidating, or overwhelming endeavour, particularly for repository teams that may be under-resourced, lack in-house technical expertise, or face capacity issues. This poster addresses digital preservation needs and workflows in IRs through one solution to those shared challenges: Scholaris - a new Canadian national, opt-in shared repository service built on the DSpace platform and centrally hosted and managed by Scholars Portal at the University of Toronto. The poster shares results from a nation-wide needs assessment survey of repository managers conducted by the Scholaris Digital Preservation Expert Group this past winter to gather insights on current practices, capacity, and needs related to digital preservation, and describe how the group is using this valuable feedback to inform the development of flexible preservation pathways within the Scholaris service model and resources, such as explainers, toolkits, and documentation, to meet these needs and build capacity for digital preservation activities within the Canadian repository 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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.057 | 0.022 |
| Scholarly communication | 0.034 | 0.012 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".