Management Practices and Implementation Challenges in District Education Directorates in Ghana
Bibliographic record
Abstract
Background: Subnational actors and organizations are crucial mediators of policy implementation due to their proximity to schools. However, in low- and middle-income country contexts, little is known about their management practices and factors that shape the adoption of these practices to improve education delivery. Purpose: We explore the management context of five District Education Directorates in Ghana, and the factors that enable or constrain them to plan and implement policy. Participants: Forty-three interviews and focus groups with regional and district education officials, district political actors, and basic education school headteachers and teachers. Research Design: A qualitative study of semistructured interviews, focus groups, and education policy and planning documents. Analysis: To understand how policy implementation happens within complex, multitiered bureaucracies, our theoretical framework uses four management functions described in Williams et al. (2021) to explore two different paradigms of how to change public bureaucracies: target setting and prioritization; measurement and monitoring; accountability and incentives; and problem-solving. We coded and analyzed our data based on this framework and developed district-wide narrative memos to synthesize the findings. Findings: We identify three areas of (mis)alignment in management practices: across bureaucratic levels and among actors; around clear and consistent priorities for learning; in expected actions and availability of resources. These (mis)alignments can constrain or be leveraged by districts to improve education delivery in Ghana. Implications: We argue for better prioritization of goals toward learning and the efficient allocation of funds for management practices typical of effective organizations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".