Against the Wind: Challenges and Barriers to Canadian Academic Librarians’ Instructional Practices
Bibliographic record
Abstract
Helping students learn how to navigate information—and misinformation—is asimportant as ever. The Canadian information literacy (IL) landscape continues to evolvealong with rhetorical, theoretical, and contextual developments, such as the newFramework for Information Literacy for Higher Education and the critical librarypedagogy movement. However, relatively little is known about actual changes tolibrarians’ IL practices over time, and what causes or prevents librarians to change theirpractices. This paper shares results from a twenty-year study of IL practices in Canadianacademic libraries, with a focus on the barriers and challenges to this work.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.045 | 0.013 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".