Overseeing Large-Scale Graduate and Undergraduate-Driven Digital Learning Projects
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
Drawing from a large-scale digital and community-based research initiative, titled Italian–Canadian FOODWAYS, this paper presents a bifold reflection on the implementation of Digital Learning tools in undergraduate studies as well as on opportunities for graduate-student-led project management. Practices will be showcased through two courses on offer at the University of Toronto Mississauga: Italian Culture through Food, a second-year Culture Studies course, and Research Opportunity Program initiatives—for-credit, upper-year research-intensive experiences. Particular emphasis will be given to highlighting practices channelled through a sustainable research loop that has yielded significant achievements both for students enrolled in the courses and for graduate students in supervisory positions overseeing project contributions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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