Dr. Meagan Troop; Manager, Educational Development
Bibliographic record
Abstract
Dr, Meagan Troop has worked in the field of educational development for over a decade with extensive experience in both college and university contexts. In her most recent role as the Manager of Educational Development at Sheridan, Meagan has collaborated with faculty, staff, and administrators to design and facilitate programs and initiatives that build teaching and learning capacity. Before joining Sheridan, Meagan worked as an educational developer and instructional designer with positions at the Universities of Guelph, Waterloo, and OCAD, and teaching experience from St. Lawrence College, Wilfrid Laurier University, and The University of Guelph in the disciplinary areas of music education, musical theatre performance, and improvisation. Meagan holds a PhD in Education from Queen’s University with a focus on curriculum, teaching and learning. She is actively involved in the Scholarship of Teaching and Learning (SoTL) as a researcher and consistently contributes to the field of educational development through peer-reviewed publications, mentorship, and educational leadership at local, national, and international conferences and events. She is an Editorial Board member of the Canadian Journal for the Scholarship of Teaching and Learning (CJSoTL), a co-chair elect of the Council of Ontario Educational Developers (COED), and the Co-Chair of the eCampus Ontario Advisory Board.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.038 |
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".