Influence of Discovery Search Tools on Science and Engineering e-books Usage
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
Influence of Discovery Search Tools on Science and Engineering e-books Usage Mr. Eugene Barsky, University of British Columbia Eugene Barsky is a Science and Engineering Librarian at the University of British Columbia (UBC). He is interested in engineering information, data management in the physical sciences and has published extensively in the library literature. Sarah Jane Dooley, Dalhousie University Sarah Jane Dooley is Head of Reference & Research Services and Promotions & Liaison Librarian at Dalhousie University’s Sexton Design & Technology Library in Halifax, Nova Scotia, Canada. Mrs. Tara Mawhinney, McGill University Tara Mawhinney is the liaison librarian for Civil Engineering and Applied Mechanics, Mechanical Engineering, and Atmospheric and Oceanic Sciences at McGill University’s Schulich Library of Science and Engineering in Montreal, Quebec. Her research interests include new technologies for collection development in science and engineering librarianship, information literacy and social networking sites for teaching and research. She completed an MLIS from McGill’s School of Information Studies in 2005. Zoey Peterson, University of British Columbia Zoey Peterson is an MLIS candidate and a student librarian at several libraries, including the Science & Engineering Library at the University British Columbia. Mrs. Michelle Spence, University of Toronto Michelle Spence is a Reference & Instruction Librarian at the University of Toronto’s Engineering & Computer Science Library. She holds a H.B.Sc. (2004) and a M.I.St. (2007), both from the University of Toronto. She has held positions in academic and public libraries, as well as a corporate setting.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 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