Authentic learning across disciplines and borders with scholarly digital storytelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholarly digital storytelling combines academic research and digital skills to communicate scholarly work within and beyond the classroom. This article presents three case studies that demonstrate efforts to integrate scholarly digital storytelling, a technology-enhanced assessment, across disciplines, geographic locations, and teaching contexts. The case studies originate in the United States, Northern Ireland [UK], and Norway, and represent learning across multiple disciplines, including history, higher education, geography, and biology. This article explores the potential for scholarly digital storytelling, when carefully planned, scaffolded, and implemented, to engage students in authentic learning, teaching students to think deeply and creatively about disciplinary content while creating sharable digital products.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it