From the Formulation of a National Policy to the Compilation of Social Protection Actions: A Case of ‘Non-design’ in Burkina Faso
Bibliographic record
Abstract
To improve the social protection of its population, Burkina Faso adopted a national policy in 2012. This paper analyses the process whereby this policy was formulated, looking at the issue from the standpoint of ‘policy design’ (Howlett and Mukherjee, 2014). Conducted in accordance with an inductive qualitative approach, the collection and analysis of the data show that this process of formulation has led neither to reflecting on the problem to be solved nor to identifying the specific needs of the beneficiaries. Nor has it led to evaluating the potential outcomes of the proposed solutions in order to choose the most appropriate ones. The authors are thus led to an empirical observation of ‘non-design’. This policy boils down to a document whose all-encompassing content brings together every conceivable action of social protection, without any arbitration. Three factors have contributed to this non-formulation: (1) the lack of clear government direction to guide discussions; (2) a weakness of support and of political will, resulting in a low degree of involvement in the process on the part of high-level decision makers; and (3) conceptual and technical misunderstandings on the part of national stakeholders in social protection—so much so that they have simply relied on the advice of international bodies. The government announced its intention of playing a leading role in the process of formulating this policy, but this was a purely rhetorical declaration. The study shows that leadership and political will have been lacking, particularly when it has come to channelling the respective interests of the stakeholders and managing the contradictions that hinder the formulation of a coherent policy adapted to the needs of the population.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| 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".