3M Fellows Making a Mark in Canadian Higher Education
Bibliographic record
Abstract
The 3M National Teaching Fellowship program has a rich history in Canada as the premier teaching award, coveted by university professors and post-secondary institutions alike. This program was developed in 1985 through a unique partnership with the Society for Teaching and Learning in Higher Education (STLHE) and 3M Canada. It has evolved into one of the most successful public/private partnerships in Canada. While the Fellowship Program has expanded and strengthened over the years, the original vision of celebrating teaching excellence and leadership in teaching continues to distinguish it from other national award programs. Each year, 10 new individuals are chosen to join the Fellowship through the submission of a detailed nomination package, which in turn is adjudicated by a rigorous selection process. Unlike the UK National Teaching Fellowship Scheme, the European Award for Teaching Excellence, or the Australian Awards for University Teaching that offer significant monetary benefits, the 3M Fellows are not awarded money. In addition, while self-nomination is not encouraged, increasingly institutions nominate their recent award winners, especially when they have been recognized for teaching internally and by regional and provincial bodies. So, why do the 3M Fellowships receive nominations year after year and why are they perceived to be more prestigious than ever before? This case study reveals why by highlighting the history of this award, the selection process, and the multiplier effect of the community of 3M Fellows. Further, the authors distinguish the salient aspects of the 3M Fellowship Program from other award schemes in higher education.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".