The Library as OER Publisher: supporting OER creation on campus
Bibliographic record
Abstract
Today many libraries are seeking new creative partnerships with faculty in open textbook and OER creation. This presentation reviews a case study at a library that is in initial stages of establishing an open textbook publishing program. At Kwantlen Polytechnic University (BC, Canada), the Library set up a suite of services in order to support our faculty in the creation of OER and to help enable Zero Textbook Cost courses. We started small with an internal open education grant to test the concept of ‘Library as Open Publisher’. Based on that small success, we followed by extending the service through an expanded granting opportunity with the library taking on publishing projects in larger and various capacities. We found that services, once offered, were met with unexpected enthusiasm and evolved in unexpected ways. In this presentation, we explain our processes and workflow to date for creating the library as open publisher. In this new publishing environment, what are we seeing and how do we recommend starting up these services? What strategies accelerate or hinder progress? We will discuss practical approaches, philosophical conundrums, and a general overview of our service in its early stages. How can we manage expectations? What will be our capacity to sustain it? Come and find out!
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.031 | 0.023 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.020 |
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".