Nuts and Bolts of Educational Policy and Educational Governance: Unpacking the Nexus between the Two through a Holistic Educational Policy-Governance Approach
Bibliographic record
Abstract
Abstract: Just like that ancient riddle about who came first between the egg and the chicken, researchers interested in educational policy and educational governance keep arguing whether educational policies guide institutional governance or institutional governance controls educational policy formation, implementation, and reform. Although there is consensus about the interconnectedness between the two, controversies about their roles in each other’s establishment and functioning continue to surface in literature on the topics and researchers remain interested in investigating the nexus between them. However, there is still confusion about what to consider when performing an educational policy-governance analysis. This paper discusses the relationship between educational policy and educational governance, and the debates surrounding their influence on each other. The findings of our literature review reveal that whether it is educational policy or governance, they can be categorized into three levels (top-down, bottom-up and middle-out) in terms of planning, implementation and review. Although traditional educational policy-governance analysis that usually focuses on one of the three levels may help understand each level separately, this paper proposes a holistic educational policy-governance approach (HEPGA) that can be useful in considering the complexity, interconnectedness and collectivism that characterize contemporary educational policy-governance practices.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| 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".