Canadian Public Library Pandemic Response: Bridging the Digital Divide and Preparing for Future Pandemics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.046 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it