Expanding digital academic library and archive services at the University of Calgary in response to the COVID-19 pandemic
Bibliographic record
Abstract
Despite the uncertain challenges facing libraries of all types during the COVID-19 pandemic, new best practices and innovative ways of approaching services have emerged. Including the groundbreaking Taylor Family Digital Library in 2011, the University of Calgary Libraries and Cultural Resources has been contributing towards the ongoing development of the digital academic library. The COVID-19 pandemic has necessitated a rapid leveraging of digital skills, platforms, expertise, and models of service delivery to continue providing exceptional and transformative experiences for the University of Calgary community. The initiatives discussed in this article include online work teams, virtual 360-degree tours, the online library chat service, digital collections agreements, and remote services for archives and special collections.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".