Educational development partnerships and practices: Helping librarians move beyond the one-shot
Bibliographic record
Abstract
Given the current, widespread concern about “fake news” and information disorder, those working in post-secondary contexts have recognized a pressing need to develop students’ digital literacy (DL). Based on our experience collaboratively designing and delivering a faculty workshop on “Teaching Students about Fake News,” we see library connections to educational development as one way to address this need. Because faculty members design, develop, and deliver the requisite curriculum—and are often called upon to address the challenges that their students face in navigating, evaluating, and applying online content—they are frequently a first point of contact for help. Research examining student interactions with online news, social media, and other digital content also demonstrates how faculty play a vital part in facilitating and supporting critical digital engagement. All of this underscores the importance of faculty roles in promoting digital and information literacies. A fruitful strategy for librarians to build better connections with faculty is through educational development strategies.
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.006 | 0.064 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".