Initiating Innovation in Post-secondary Institutions – Customizing Teaching and Learning Environments: Collective Reflections from the 2014 Cohort of 3M National Student Fellows
Bibliographic record
Abstract
In light of the enormous changes unfolding presently in the higher education landscape, we don’t have to look too far to recognize evidence of the transformation and redefinition of the construct of both teaching and learning in the information age. With a growing focus on teaching and learning at all levels of post-secondary institutions, innovation is reflective in the introduction of new learning spaces, state-of-the-art technology-enhanced education, and prominence given to discussions about adapting teaching and learning to the 21st century. Likewise, in this article we examine the reflections, ideas, conversations and exchanges inspired by the cohort’s plenary planning discussions and the current birth of innovation in reshaping Canadian 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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.034 | 0.024 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".