Creative write-ins at academic libraries: Fostering virtual and in-person space for writers and the potential for multi-directional learning
Bibliographic record
Abstract
In 2022, the University of Saskatchewan's University Library in Saskatoon, Canada launched the Creative Write-Ins program, which invites creative writers from the university and the broader community to come to the university library to work on their projects. The two-hour, monthly program adopted an informal community of practice (CoP) model, since it allowed for multi-directional learning along the spectrum of participants, from experienced writers to hobbyists. This paper will discuss the intrinsic case study of these hybrid creative write-ins through the lens of Wenger et al.'s (2002) CoP model. Reflections include the lessons learned, areas for improvement, perspectives on how academic libraries can collaborate with external partners, and reflections on how this program demonstrated the potential for multi-directional learning.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".