Innovation Leadership in Polytechnics Beyond COVID: Sensemaking and Transformation in an Age of Uncertainty
Bibliographic record
Abstract
The COVID pandemic has and continues to affect us all. It is the all-encompassing catastrophe that has forced us to face uncertainty and question our values and face our challenges in every aspect of society, including academia. It has also been the commonality that binds us all together, our shared experience in an age of uncertainty. For Polytechnics overall, it has also been a call-to-action and has clearly demonstrated our ability to innovate, adapt and overcome. Whether it has been the transitioning to an online, remote workforce or the accelerated use of new technological advances in education, Polytechnics have embraced these challenges and pivoted to meet the needs of students, faculty and industry. As such, Polytechnics have continued to emerge, now well positioned to prepare Canada for a prosperous future and growth through insights learned during the pandemic and innovative new educational program offerings and formats. This paper discusses the critically important role of innovation leadership in Polytechnics beyond COVID. Starting with an example of innovation, I will introduce the new interdisciplinary Centre for Digital Transformation (C4DT) at the British Columbia Institute of Technology. This centre brings people together from across various distinct communities of practise and expertise to confront complex modern challenges through a process of communication, collaboration and sensemaking. Then merging significant literature review, I will examine recent success stories and practise transformations that shine light on Polytechnics innovation leadership role beyond COVID. Finally, I will examine the future of Polytechnics in an age of uncertainty and challenge readers to reflect upon what they have learned over the past year and consider the question, How can their own Polytechnics embrace these insights through sensemaking and transformation to embrace innovation leadership beyond COVID?
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.051 |
| Scholarly communication | 0.045 | 0.025 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.007 | 0.015 |
| 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".