Leading Large Scale Innovation: Building Institutional Flexibility
Bibliographic record
Abstract
RE-Engineered was launched at the University of Saskatchewan in 2021/22. It was designed to build community among our first-year engineering students with modularized courses, full integration across all learning outcomes and courses, competency-based assessment, introduction to 4 sciences instead of the usual 2, an Indigenous Cultural Contextualization module, and replacement of final exams in December with a week of experiential learning days across 5 engineering disciplines. The scale of the changes envisioned by the first-year team (Sean Maw and Joel Frey) was so large that it impacted most institutional support units and had substantial operational and teaching practice change requirements for two colleges. Over the four-year design process, it became clear that the curricular design required a parallel and intentional process of broad organizational change for successful implementation. From this realization sprung the Change Management Committee (CMC). This group has leveraged resources (financial, human, expertise), influenced key decision makers on campus, and facilitated deep organizational change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".