Bibliographic record
Abstract
<p>Toronto Metropolitan University Library implemented a 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 service was 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 TMU librarians assessed this service to understand its impact and plan for future service offerings. </p> <p><br></p> <p>The purpose of this assessment was to understand 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? As the researchers found the service was impactful, namely in reducing barriers to academic research related to travel, health, and ease of access. These results have informed the future of this service at TMU Libraries and can be used to as a starting point for assessment by academic libraries regarding similar services implemented mid-pandemic. </p> <p><br> This collections contains information related to the Scan and Delivery service assessment, including a paper delivered at the Library Assessment Conference in November 2022. </p>
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".