Assessing Scan and Deliver During COVID-19 and Beyond Conference Proceedings
Bibliographic record
Abstract
This paper was delivered to the Library Assessment Conference in November 2022. Abstract: In the summer of 2020, the Toronto Metropolitan University (TMU) Libraries started a digitization service called Scan and Deliver amidst the COVID-19 pandemic. Since the Library building was closed to patrons with limited on-site staffing, the library provided some access to parts of the print collection when electronic alternatives were unavailable. The Scan and Deliver service allowed patrons to request a portion of text, such as a chapter of a book or journal article, to be scanned by a Library staff member and emailed to them. When the Library building reopened to patrons in Winter 2022 we assessed this service to understand its impact and plan for future service offerings. This paper addresses why Toronto Metropolitan University Libraries patrons have used the Scan and Deliver service: what benefits does it offer them, what role do scanned materials play in their scholarly research, and what barriers does it help them overcome? The results of our assessment show that the Scan and Deliver service has been impactful for patrons. Specifically, students told us it enabled them to complete their coursework and instructors stated that it allowed them to plan coursework and conduct research. The service helped patrons overcome barriers to completing their academic pursuits during the COVID-19 pandemic, including limits related to travel and serious health concerns. The service has also increased access to the collection and patrons describe it as easy to use and convenient. They also noted a few areas for service improvements. Patrons viewed the service as working in conjunction with other library services, such as interlibrary loan and course reserves, as a method of extending access to print. As a result of this analysis Library administration extended the scanning service for an additional year due to its value to patrons, allowing additional time for ongoing assessment.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".