Making a Difference: Three Decades of Canada’s Only National Teaching Award
Bibliographic record
Abstract
The 3M National Teaching Fellowship (3MNTF) has been part of the Canadian higher education landscape for the past 31 years and has grown to be a community of 328 fellows, with up to ten more being added each year. As part of the 30-year anniversary of the Fellowship, we initiated a study to understand its impacts on the higher education community by examining the effect that the Fellowship has had on individual winners, the influence that fellows have been able to exert in their institutions after being awarded the 3M Fellowship, and the influence that the 3M National Teaching Fellowship program has had nationally and internationally. To identify the various impacts of the 3MNTF, we conducted focus groups with the 2012 cohort, 3M retreat facilitators and coordinators, and the representative from 3M Canada, as well as the new fellows from the 2013 cohort. In 2014, we conducted two focus groups with senior university administrators and educational developers. In 2015, we developed and administered an online survey to faculty, administrators, educational developers, and students at a number of Canadian universities. We found that the 3M is one of the most recognizable teaching awards in Canada’s higher education landscape. The structure of the fellowship has helped to shape local and international teaching awards, while individual fellows often provide mentorship to future leaders in education.
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How this classification was reachedexpand
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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".