Bibliographic record
Abstract
Like many other academic libraries, my home institution has been reflecting on what role we should play in the changing research ecosystem.The opening of our new Riddell Library and Learning Centre in 2017 changed the services and spaces our campus has come to associate with the Library.On any given day, nursing faculty may be using our new 360 Degree Immersion Studio to simulate a busy emergency room while geography faculty take advantage of our visualization wall to examine satellite imagery up close with their students.As our faculty and students take advantage of these new digital tools and platforms in their research, teaching, and learning, we as librarians wonder if we are doing enough to support this work and what we should focus on going forward.In The Culture of Digital Scholarship in Academic Libraries, editors Robin Chin Roemer and Verletta Kern have drawn together a collection of case studies of how digital scholarship is being defined and operationalized within one academic institution -the University of Washington (UW).All but one of the chapter authors are currently (or were recently) employed by UW Libraries, a sprawling library system with multiple locations, hundreds of employees, and a budget of over 50 million dollars annually [1].Each author approaches the topic through the lens of their particular role within the institution (e.g.
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 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".