Librarian-Lead Faculty Learning Communities Offer Opportunities for Collaboration
Bibliographic record
Abstract
A Review of: Burress, T., Mann, E., & Neville, T. (2020). Exploring data literacy via a librarian-faculty learning community: A case study. Journal of Academic Librarianship, 46(1). https://doi.org/10.1016/j.acalib.2019.102076 Abstract Objective – To describe a librarian-lead faculty learning community (FLC) focused on data literacy. Design – Case study. Setting – A public university in Florida. Subjects – 10 participants in the FLC. Methods – Two librarians proposed the Data Literacy Across the Curriculum FLC as part of the University of South Florida St. Petersburg Center for Innovation in Teaching and Learning. Participants were recruited from all full-time instructional faculty. The group met for monthly 90-minute meetings throughout the fall and spring semesters. Meetings were focused on group goal-setting, lightning talks, open discussion, data tool demonstrations, and the planning and development of work projects. In addition, the group designed an informal survey on the use of data tools across the institution. Main Results – At the conclusion of the year-long FLC, the group developed a frame for data literacy competencies that can be utilized across the curriculum. The FLC participants created a Data Literacy Faculty Toolkit that presented that theoretical framework, as well as providing sample activities and other resources to help faculty to practically implement that framework into their instruction. The student success librarian also integrated data literacy into the first-year student information literacy curriculum. Conclusion – Participation and facilitation of the FLC by librarians served to further librarian-faculty collaboration, as well as demonstrating library value. The work of the Data Literacy Across the Curriculum FLC raised awareness about information and data literacy on campus, and provided support to faculty members looking to further integrate data literacy into their instruction.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.028 |
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".