Good governance practices in Ghana’s FLEGT voluntary partnership agreement process: an application of Q methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of the European Union Forest Law Enforcement, Governance, and Trade Action Plan and its Voluntary Partnership Agreements (VPAs) is to tackle illegal logging and trade in illegal timber, improve forest governance, and foster economic growth in the forest sector. This study employs a Q methodology to assess areas of consensus and disagreement among forest sector stakeholders in Ghana on good forest governance practices as applied to the VPA process. The consensus among these stakeholders is that the VPA process has improved the participation of civil society in decision making and the establishment of a robust verification system to promote transparency and accountability, which are critical for sustainable forest management. However, while the shared perspectives among stakeholders highlight the crucial role of the VPAs in promoting improved forest governance in Ghana, there are still areas of disagreement or tension regarding the issue of accountability, tree tenure rights, and the participation of local communities in the VPA process. The paper concludes by considering the practical implications of the findings for effective forest governance practices in developing countries.
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.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it