Leading Transformation with Digital Innovations in Schools and Universities: Beyond Adoption
Bibliographic record
Abstract
Abstract Digital innovations in schools and universities matter. New leadership approaches and new organization knowledge are necessary for leaders to realize long-term school and university transformations afforded by important digital innovation experiments. This chapter takes a hard look at leadership and organization theory and practice, along with a critical look at innovation adoption to help digital school and university innovation teams find more sustainable, impactful innovations. First, we examine research and theory on formal leadership and organization to argue that classical, formal leaders separate people from the work of others, limiting innovation teamwork. We also examine formal organizations as “houses,” finding that these over-structure people and power in vertical functional “boxes” in bureaucracies that limit school or university readiness to adapt—even when great digital innovations offer transformation potential. Less formal leadership and organization is then explored with evidence from the author’s research on leading complex adaptive teams as more adaptable organization network forms. We conclude that less formal leadership and less formal organizing structures offer more innovation potential by creating adaptive spaces for digital innovations. We present a new theory and guidelines for leading and participating in high-impact digital innovation networks working to lead learning organization emergence (transformation) via digital innovations.
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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 | 0.001 | 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 | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".