CULTIVATING COMMUNITIES OF PRACTICE ON A NATIONAL SCALE TO SUPPORT THE SHIFT TO REMOTE EDUCATION
Bibliographic record
Abstract
Educational innovations and just-in-time supports spread more quickly through social networks thanthrough traditional dissemination avenues. Therefore, in coordinating national level support efforts for the shift to online and remote learning during the COVID-19 pandemic, one of the principal strategies of theEngineering Collaboration for Online and Remote Education (E-CORE/CIEL) Project was to developnational Communities of Practice (CoPs) to foster connections between instructors. Using an autoethnographic process, this reflective paper aims to synthesize the learnings from the team working to cultivate these CoPs. The analysis of the reflections provides insight on: the needs of the Canadian community of engineering educators during a year of remote education, the perceivedbenefits of engaging in CoPs, considerations for cultivating CoPs in different contexts, andrecommendations for future cross-institutional CoP efforts.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".