Canadian Public Library Pandemic Response: Bridging the Digital Divide and Preparing for Future Pandemics
Bibliographic record
Abstract
This article examines the impact of the COVID-19 pandemic on computer and Internet access services in Canadian public libraries as well as the implications of this lack of access for people facing socioeconomic barriers, and how Canadian public libraries could address digital divide issues in the post-pandemic era. Recommendations on future pandemic preparedness for public libraries are also discussed in this article. This research project conducted a bilingual (English and French) online survey targeting public library technicians, librarians, and library board members across Canada. From 1,631 research invitation emails sent to public library staff across Canada and three Facebook posts on Canadian public library staff groups, over a one-year period from November 3, 2021, to November 6, 2022. 226 individuals participated in the online survey questionnaire. Findings suggest that the COVID-19 pandemic has exacerbated social inequalities in Canada, including access to computers and the Internet. The digital divide could lead to poor health outcomes and put existing disadvantaged populations at greater risk in terms of future employment opportunities. The digital divide needs to be addressed so that Canadians in low-income households and those living with disabilities do not get left behind. Importantly, public libraries in Canada have been working tirelessly to equalize access to computers, the Internet, and digital literacy training and support. Their determination, social responsibility, and professional ethics need to be acknowledged. Finally, this article's recommendations for future pandemic preparedness in Canadian public libraries may also be applicable and beneficial to public libraries globally.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".