The Summer of the Pivot: Prioritizing Equity in Remote Instruction through a Multidisciplinary Community of Practice Initiative at a Canadian University
Bibliographic record
Abstract
This article is about the multidisciplinary Community of Practice (CoP) initiative that was implemented in the summer of 2020- summer of the pivot- at a Canadian post-secondary institution to prepare faculty, staff, and students for remote teaching and learning while navigating pandemic conditions created by COVID-19. The CoP as a case study using Critical Theory as a theoretical framework examines the experiences of a collective group of faculty and staff from different disciplines leading a multidisciplinary university-wide initiative and the implications of the approach for promoting effective pedagogies for teaching and learning remotely. Findings based on feedback from workshop attendees, reflections from the CoP facilitators, and comments forwarded to senior administrators about the impact and the effectiveness of the program indicate positive results. It is recommended that although the CoP initiative was originally conceived as a response to the summer of the pivot, it should become an integral approach to promoting dialogue and innovative strategies to advance equitable practices in higher education by cultivating community networks. The findings serve to continue constructive dialogues and discussions about how universities can transition, pivot, and mobilize innovatively and creatively to prioritize equitable teaching and learning conditions that challenge the status quo. This requires a long-term commitment by higher education institutions to break away from historically normalized practices and invest in innovative ways to identify and meet the needs of various stakeholders.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".