Beyond the Champion – Governance and Management of Strategic Innovation in Higher Education Teaching and Learning
Bibliographic record
Abstract
Abstract As a sector, higher education is at the low end of innovation rankings. The challenges we face – demographic, technological, political, and pedagogical – will require sustained innovation at a strategic level. Recent research with mature companies has identified exemplars in strategic innovation (e.g., O’Connor, Corbett, & Peters, 2018). This work explores whether – and how – higher education institutions might adapt insights from the corporate sector for strategic innovation in teaching and learning. The introductory section provides an overview of the nature of strategic innovation (and why it is hard to sustain), strategic issues facing higher education, and the status and challenges of sustaining strategic innovation for teaching. The next two sections describe insights from research with corporate exemplars of sustaining strategic innovation. Each section uses a scenario from higher education as a proof-of-concept test to explore the application of the corporate sector insights for strategic innovation in higher education teaching and learning. The final section of the chapter discusses the planned next steps to prototype and test adaptation of these corporate sector insights with institutional innovation leaders in higher education, as well as additional potential sources of insights (from other research in the corporate sector and from strategic innovation in the public sector).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".