Investigating ACF Policy Change Theory in a Unitary Policy Subsystem: The Case of Ghanaian Public Sector Information Policy
Bibliographic record
Abstract
In 2019, the government of Ghana overhauled its access to public information rules through the Right to Information Act. Prior to this legislation, access to public sector information was not formally regulated and the new legislation provided a legal framework for making public sector information accessible to the general public. From an Advocacy Coalition Framework (ACF) perspective, the passage of the Right to Information Act represents a major policy change and provides a case in which the ACF theory of major policy change can be investigated. This case is also interesting because it took place in a unitary policy subsystem, as opposed to a competitive or collaborative subsystem. Unitary subsystems are characterized by a single, dominant advocacy coalition, in this case a pro-transparency coalition, and are relatively uncommon in the ACF literature. The purpose of this paper is to investigate ACF policy change theory in the Ghanaian public sector information policy subsystem – as a unitary subsystem – to determine whether it can explain the major policy change that took place with the passage of the Right to Information Act. The investigation finds strong empirical support for the ACF’s ‘pathways’ hypothesis and moderate support for the ‘power’ hypothesis.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".