Academic libraries and the pandemic: lessons learned and future plans
Bibliographic record
Abstract
In this video poster presentation, the presenters will discuss how their library, at an overseas Canadian university, responded to the COVID-19 pandemic, as well as share what their “new normal” looks like. While our library’s function as a physical hub was severely hampered, it continued to play its role as a resource and saw more and more patrons turn to it for academic support. Given the disruption of normal library services, including the opportunity to seek information through the physical library help desk, blended services were offered through restricted physical library access and digital resources. Parallel to online class delivery, the library introduced an increased number of electronic devices, discussion forums, course-specific digital resources and chat services for engaging our university community. Even though we are now once again engaging face-to-face, there remains a need for instruction and resources to navigate an online environment (Ziv & Bene, 2022). In the case of students, especially those suffering from social inequalities, it can be harmful to buy into the myth that they are digital natives (Enyon, 2020), with the know-how to operate in and analyze online environments. Similarly, faculty may no longer need the level of support they did during emergency remote teaching, but there is always room for improving teaching-with-technology methods. The pandemic cemented the core role libraries play in promoting and supporting information and digital literacy. The presentation will discuss the practices that the library is consciously continuing with post-pandemic. References Enyon, R. (2020). The myth of the digital native: Why it persists and the harm it inflicts. In Burns, T. and F. Gottschalk (Eds.), Education in the Digital Age: Healthy and Happy Children (pp. 131 - 143). OECD Publishing. https://doi.org/10.1787/1209166a-en Ziv, N., & Bene, E. (2022). Preparing College Students for a Digital Age: A Survey of Instructional Approaches to Spotting Misinformation. College & Research Libraries, 83(6), 905–925. https://doi.org/10.5860/crl.83.6.905
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".