From Bricks and Mortar to Bits and Bytes: Examining the Changing State of Reference Services at the University of Toronto Libraries During COVID-19
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.017 |
| 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