Institutional Barriers to Climate Change Adaptation in Burkina Faso: How could We Go around Them?
Bibliographic record
Abstract
Despite the existence of a National Adaptation Plan to climate change (NAP) in Burkina Faso, operationalizing adaptation still face a number of challenges. The current study focused on identifying institutional barriers to the strategic objectives of climate change adaptation (CCA) using a literature review and semi-structured interviews conducted with key stakeholders / resource persons involved in the implementation of the NAP. The results revealed a weak collaboration between the NAP steering institution and the ministerial departments covered by the NAP. This situation, first, hampers the implementation of adaptation actions and secondly, the monitoring reporting and verification of adaptation initiatives. Further, the analysis revealed that lack of financial resources poses constraints to many actions that were to be taken by the steering institution and therefore creates poor ownership of the NAP by the main stakeholders that should be actively involved in the NAP process. To cope with the various constraints, it is necessary to have strong political support in many aspects. For instance, it was judged that institutionalizing the role of climate change (CC) focal point within the ministries and embedding NAP monitoring and evaluation (M&E) objectives and indicators with existing functional M&E systems in the sectorial ministries will ease CCA actions integration in operational plans, their implementation and documentation. Moreover, it is relevant to have a continuous capacity building plan to keep stakeholders updated on climate change issues as this will support them in their mandate of mainstreaming CC into ministerial operational plans and lead to optimal CCA implementation and monitoring.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".