Engaging health sciences librarians on data ethics: case study on a pilot curriculum
Bibliographic record
Abstract
Background: Ethical decision-making regarding data collection, visualization and communication is of growing importance to librarians. Data ethics training opportunities for librarians, however, are uncommon. To fill this gap, librarians at an academic medical center developed a pilot data ethics curriculum for librarians across the US and Canada. Case Presentation: Three data librarians in a health sciences library developed a pilot curriculum to address perceived gaps in librarian training for data ethics. One of the team members had additional academic training in bioethics, which helped to provide an intellectual foundation for this project. The three-module class provided students with an overview of ethical frameworks, skills to apply those frameworks to data issues, and an exploration of data ethics challenges in libraries. Participants from library schools and professional organizations were invited to apply. Twenty-four participants attended the Zoom-based classes and shared feedback through surveys taken after each session and in a focus group after the course's conclusion. Discussion: Responses to the focus group and surveys indicated a high level of student engagement and interest in data ethics. Students also expressed a desire for more time and ways to apply what was learned to their own work. Specifically, participants indicated an interest in dedicating time for networking with other members of their cohort, as well as more extensive discussion of class topics. Several students also suggested creating concrete outputs of their thoughts (e.g., a reflective paper or final project). Finally, student responses expressed a strong interest in mapping ethical frameworks directly to challenges and issues librarians face in the workplace.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".