Library programming in undergraduate 'Ready for Research' badge: Reflections from librarians and students
Bibliographic record
Abstract
Librarian involvement in post-secondary teaching and learning programming depends greatly on institutional context. Librarians and SoTL scholars have much in common, with librarians focusing on how students interact with the information they encounter in their academic activities. How do librarians perceive their contributions to undergraduate teaching and learning outside course-integrated instruction? And how do students respond to the information literacy skills librarians aim to impart? This year saw the launch of a micro-credential badge at University of Calgary aimed to prepare undergraduate students to be ready to engage in research activities. Organized and facilitated by the Taylor Institute for Teaching and Learning, the pilot year involved a significant presence of workshops from librarians and library staff. This presentation: (1) showcases the context leading to this collaboration and (2) highlights survey feedback and reflections from both librarians teaching and students learning in the program.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".