Bibliographic record
Abstract
While COVID-19 dramatically changed the way that we taught during the pandemic, not all of these changes were negative. In response to the pivot to remote learning, Western University employed student digital media interns (DMIs) to support faculty in adapting their courses. This resulted in the formation of the Digital Media Intern program at the Schulich School of Medicine & Dentistry (SSMD), a students-as-partners (SaP) approach that supports faculty in the adoption and use of educational technology. Despite moving back to in-person learning, the DMI program is thriving and has expanded its scope. An understanding of the learner context of technology can be missing when faculty are designing and updating their courses. The DMI program helps bridge this gap by creating a way for students to directly contribute to their education, gain meaningful employment or experience, and provide feedback to instructors. Instructors benefit in two ways: by gaining hands-on support and ongoing, actionable feedback. This case study will outline the evolution of the DMI program, its implementation and its impact. Leader and student perspectives will also be shared. It describes the evolution of this student intern strategy from a band-aid solution to a fully integrated and supported unit in one academic faculty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.140 | 0.024 |
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".