How does explicit knowledge inform policy shaping? The case of Burkina Faso’s national social protection policy
Bibliographic record
Abstract
In 2009, Burkina Faso embarked on a process leading to the development of a national social protection policy (politique nationale de protection sociale-PNPS) in 2012. The objective of this study was to analyze the circumstances under which explicit knowledge was used to inform the process of emergence and formulation PNPS. The term explicit knowledge excludes tacit and experiential knowledge, taking into account research data, grey literature, and monitoring data. Court and Young's conceptual framework was adapted by integrating concepts from political science, such as Kingdon's Multiple Streams framework. Discursive and documentary data were collected from 30 respondents from national and international institutions. Thematic analysis guided the data processing. Results showed that use of peer-reviewed academic research was not explicitly mentioned by respondents, in contrast to other types of knowledge, such as national statistical data, reports on government program evaluations, and reports on studies by international institutions and NGOs, also called technical and financial partners (TFPs). The emergence phase was more informed by grey literature and monitoring data. In this phase, national actors deepened and increased their knowledge (conceptual use) on the importance and challenges of social protection. The role of explicit knowledge in the formulation phase was nuanced. The actors' thinking was little guided by the question of whether the solutions had the capacity to solve the problem in the Burkina Faso context. Choices were based very little on analysis of strategies (effectiveness, equity, unintended effects) and their applicability (cost, acceptability, feasibility). This way of working was due in part to actors' limited knowledge on social protection and the lack of government guidance on strategic choices. Strategic use was clearly identified. It involved citing knowledge (reports on studies conducted by TFPs) to justify the utility and feasibility of a PNPS. Instrumental use consisted of drawing from workshop presentations and study reports when writing sections of the PNPS. The consideration of a recommendation based on explicit knowledge was influenced by perceived political gains, i.e., potential social and political consequences.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.007 |
| 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".