The leadership of innovation in education: findings from an environmental scan
Bibliographic record
Abstract
Although innovation is widely promoted as essential to educational transformation, definitional ambiguity and a persistent disconnect between policy rhetoric and implementation realities leave educational leaders without the conceptual clarity or systemic support necessary to enact meaningful change. This article presents findings from a global environmental scan that explores how innovation in education is defined, enacted, and constrained through educational leadership. Guided by systematic screening standards, the scan initially identified 147 grey literature sources, of which 94 met the inclusion criteria. Using comparative and thematic analysis, the study examines how educational leadership mediates innovation across diverse governance, policy, and cultural contexts. Three key themes emerged: (1) the enabling and constraining conditions for innovation; (2) the persistent tensions leaders face between top-down mandates and grassroots responsiveness; and (3) the global variation in leadership strategies shaped by sociopolitical and economic conditions. These findings highlight a significant disconnect between policy rhetoric and leadership realities, as well as a lack of definitional clarity and systemic support for innovation. This study calls for more context-responsive, relational, and adaptive leadership frameworks that align local needs with broader reform goals.
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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".