From Bricks and Mortar to Bits and Bytes: Examining the Changing State of Reference Services at the University of Toronto Libraries During COVID-19
Bibliographic record
Abstract
Before the pandemic, the University of Toronto was predominantly an in-person experience. The closure of physical libraries and shift to remote learning required library staff and users to adapt to new modes of supporting teaching, learning, and research. A survey was conducted about reference service delivery, staffing models, resources and tools, which asked the respondents to describe reference services at their libraries before and during the pandemic. The objectives of this survey were to capture the state of reference services at the University of Toronto Libraries (UTL), and to compare data about reference practices during the pre-pandemic and pandemic periods with the goal of identifying challenges and opportunities for the future of reference services at UTL. 70% of libraries surveyed used reference desks for reference services pre-pandemic, and during the pandemic, 75% of libraries used virtual reference appointments by video conferencing. The survey results show that reference service staffing and service hours in most surveyed libraries were reduced during the pandemic. Many respondents reported that while they offered fewer reference service hours during the pandemic, they continued to provide assistance outside of scheduled hours. Online tools and platforms that were already familiar to librarians remained popular during the pandemic, allowing service providers to quickly adapt to the virtual environment and ensure seamless service continuity. While the rapid transition in services at the University of Toronto was not without its challenges, it has also offered many new opportunities for re-envisioning reference services at the University of Toronto Libraries.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".