Rapport final du sondage sur la capacité de préservation numérique et les besoins des institutions de la mémoire au Canada, 2017-2018
Bibliographic record
Abstract
The digital preservation landscape is complex and rapidly evolving, and memory institutions are increasingly looking for solutions to address the substantial challenges associated with their emerging digital stewardship responsibilities. The Canadian Association of Research Libraries' Digital Preservation Working Group (CARL DPWG) sought input from libraries and archives across Canada on the state of their digital preservation capacity and the gaps in their organizations. The goal of the survey was to provide for an accurate and updated picture of digital preservation activities in Canada, and to help to identify existing gaps and outstanding needs at Canadian institutions. Ultimately, it is anticipated that the findings will contribute to the development of strategies, policies, expertise and resource allocation that will enable the community to build capacity and help to ensure that Canada’s valuable digital assets are preserved for future generations. The survey was undertaken in two phases: Phase 1 targeted CARL members in 2017 (with an update in 2018) and Phase 2 targeted other Canadian memory institutions in 2018. A total of 52 complete responses were received.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".