Deliberation and decisionism in educational policymaking: How Nepali educational policymakers negotiate with foreign aid agencies
Bibliographic record
Abstract
In the countries that receive aid from donor agencies, the educational policymaking process is not straightforward because the power and interest of donors contradict with national contexts. This qualitative study aims to investigate how educational policy decisions in Nepal, a country that receives foreign aid for its educational projects, are made. Drawing on the Habermasian conceptualisation of deliberative democracy, I theorise that educational policy decisions are made either through deliberation or decisionism. An analysis of interviews conducted with educational policymakers of Nepal found that policymaking in Nepal follows decisionism in which the representatives of foreign aid agencies are more dominant than national bureaucrats. Even though Nepali bureaucrats and political leaders are involved in the decision-making process, rational interactions do not happen because they want to fulfil their personal interests by endorsing the decisions determined by the donors. This study concludes that because of decisionism, neocolonialism, and dysfunctional policy sphere, teachers, students, parents, and community people are excluded in the decision-making process. The findings are significant not only for understanding the lack of deliberation in the policymaking process but also for improving the educational praxis of aid-recipient countries like Nepal.
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".