Building Dynamic Capabilities towards Innovation and Flexibility
Bibliographic record
Abstract
In March 2020 most Higher Education Institutions around the world quickly moved to online learning and work with only one week to prepare. While the pandemic forced us to do this quickly and without a well thought out plan, most of us met this challenge. The World Health Organization declared that COVID-19 was no longer a global health emergency on May 5, 2023, but many institutions had started the return to “normal” before then. This Organizational Improvement Plan starts with the vision that rather than retrenching back to “normal”, Ontario Polytechnic, a large polytechnic institution in Ontario, Canada, should be moving even further towards flexibility in teaching, learning and work to remain competitive in a complex and changing environment. It goes further to envision Ontario Polytechnic as an organization that is equipped to deal with future innovations, with the dynamic capabilities and innovation mindset to respond creatively and effectively to changes in the environment. In moving this culture change forward, this OIP outlines the importance of understanding social networks, both formal and informal, early and deep engagement of employees, who are seen as actors in the change, rather than recipients of change, and uses Kotter’s Modified 8-Step Change Management Model as a road map to change. The complementary application of both transformational and complexity leadership approaches is key to the success of undertaking a culture change as deep as this one.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".