An advocacy coalition approach to water policy change in Ghana: A look at belief systems and policy oriented learning
Bibliographic record
Abstract
The Advocacy Coalition Framework (ACF) was developed by Sabatier and Jenkins Smith in 1993 to explain and predict policy change. Bloomquist and other scholars have referred to the ACF as one of the most promising theoretical frameworks for studying the policy process. The ACF has been applied widely to policy change in a plethora of substantive policy areas in the United States, as well as in Canada, the United Kingdom and Australia. However, the ACF has not yet been applied to explain the policy process in Africa. Thus, to test the robustness of the framework, this research applies the ACF to explain water politics and the water policy process in Ghana. This research specifically looks at the belief systems and policy oriented learning in water policy change in Ghana. Using a combination of survey methodology and key interviews, the research tests two hypotheses of the ACF; i) Coalition members are more likely to interact with actors they perceive as sharing their beliefs than actors who do not share their beliefs; ii) Policy oriented learning is likely when there is the presence of a professionalized forum than when there is not. The findings show that the ACF offers a good explanation of the water policy process in Ghana. Key words: Advocacy coalition framework, water politics, Ghana.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".