Endogenous systems leadership of education provision during crises in low- and middle-income countries: A conceptual framework
Bibliographic record
Abstract
Abstract This article advances the case for an ‘endogenous systems leadership’ approach to the provision of inclusive and equitable quality education for all in times of crisis. It draws on theoretical work which addresses complexity in education systems, systems leadership in education, coloniality within education, and international partnerships in low- and middle-income countries (LMICs) to propose an endogenous systems leadership framework. This is developed and illustrated with reference to three UNESCO International Institute for Educational Planning (IIEP) case studies on the leadership of education during crisis, in Burkina Faso, Jordan, and Kenya, respectively. The framework incorporates the recognition of education as a complex system and responds to the need to democratize governance for inclusive and equitable provision. The article identifies opportunities for national and international actors in LMICs to deliver on such an approach through heuristics to inform dialogue and decision-making in relevant policy, practitioner, and research communities.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".