EDUCATIONAL LEADERSHIP CHANGE IN POLICY AND GOVERNANCE: A PANACEA FOR SCHOOL INITIATIVES IN NIGERIA
Bibliographic record
Abstract
In the Nigerian education system, organizational change is frequently misconstrued as being driven by individual or group preferences. This article aims to delve into various pieces of literature to explore how change leadership in policies and governance can be effectively implemented in schools to foster innovation. Employing a meta-framework design approach, the article thoroughly reviews change leadership domains. A synthesis of peer-reviewed journals is presented to elucidate the nexus between leadership philosophy, change, and innovations within Nigerian educational institutions. This examination is conducted through the dual lenses of process and content. The article argues that successful change leadership in school settings necessitates several key steps: identifying the need for change, engaging stakeholders to acknowledge this need, structuring the change process, fostering stakeholder commitment, implementing and sustaining change through robust planning and oversight, and nurturing individual and organizational capabilities to enable problem-solving and solution generation. Moreover, it challenges the notion that change is solely a top-down endeavour, asserting that leadership is predominantly a social practice or process rather than merely a charismatic display
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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".