Canadian Collaborations: Library Communications and Advocacy in the time of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic forced libraries to unexpectedly and suddenly close their physical locations, necessitating a remote working environment and a greater reliance on digital and virtual services. While libraries were in a better position than most sectors due to decades of experience in licensing and acquiring digital content and offering virtual services such as chat reference, there still were some services and resources that traditionally had only been offered in a face-to-face environment, or were available in print only. There were questions in the Canadian library community about how, and if these programs could be delivered online and comply with Canadian copyright law. This article will describe the access and copyright challenges that Canadian libraries faced during the first nine months of the pandemic and will outline the collaborative efforts of the Canadian library copyright community to respond to these challenges.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.073 | 0.016 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 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".