Assessing Scan and Deliver during COVID-19 and Beyond Presentation
Bibliographic record
Abstract
This is a conference presentation delivered at Library Assessment Conference in November 2022. Toronto Metropolitan University Library implemented the scan and deliver service in June 2020, during the COVID-19 pandemic. With this service patrons can request a portion of text, such as a chapter of a book or a journal article, be scanned by a library staff member and emailed to them. This and other complementary services were implemented because of limited access to the physical library building and the print collection due to pandemic lockdowns. As the Library space reopened to patrons, we assessed this service to understand its impact and plan for future service offerings. This paper addresses why Toronto Metropolitan University Library 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? These questions will inform the future of this service at our library and can be used to address similar questions facing other libraries regarding services implemented mid-pandemic.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.017 |
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".