A Sustainable Way Forward: A Team-based Approach to Tackling Textbook Access and Affordability Issues During the “New Normal”
Bibliographic record
Abstract
Like all institutions across North America, The University of Alberta Library has experienced dramatic impacts on our services and collections due to the COVID-19 pandemic. Students at our large research institution have historically relied heavily on the Library’s extensive reserve collection of textbooks and other required course materials, the lending of which was suddenly suspended during a mid-term emergency closure. This column will highlight our team-based approach to aggressively promoting OER to our campus community: from engaging public service desk staff in new roles as their work suddenly shifted, strategizing with our collections team on identifying high impact courses, and establishing a communications approach with librarians. We will discuss how our “by-the-seat-of-our-pants” initial approach has evolved into a functional team with a diverse set of strengths, and a responsive workflow that incorporates OER services as an integrated component of existing library processes.
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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 | 0.042 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.037 | 0.020 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".