Transdisciplinary Epistemic Practices as a Teaching Narrative: Redesigning a Library and Information Science course that integrates STEM, Arts, and Design
Bibliographic record
Abstract
This article reports on revisions to a course in Library and Information Science (LIS) made by the instructor, to apply, test, and evolve a transdisciplinary approach to class inquiry. This study was part of a larger research project grounded in the Scholarship of Teaching and Learning, entitled Exploring Transdisciplinary Approaches to STEM Teaching and Learning, whose purpose was to support transdisciplinary educators in justifying, evaluating, and legitimizing knowledge claims about their work through the application of a novel Transdisciplinary Epistemic Practices(TEP) model integrating elements from STEM, Arts, and Design (often referred to as STEAM). The TEP model is a conceptual tool that promises to be useful for the Critical Making stances that are common in technology courses for librarians. It was operationalized and tested in eleven courses across Pratt Institute, including a Library and Information Science course called Growing Up Digital (INFO 678), which is the focus of this paper. The first author, Leanne Bowler, was a participant in the study, applying action research methods to her own teaching practices in this class, and reporting results back to the larger project led by Mark Rosin.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".