‘With or Without You’: The Governance of (Local) Security and the Koglweogo Movement in Burkina Faso
Bibliographic record
Abstract
In late 2014 and after more than two decades of a ‘semi-authoritarian’ regime, a popular insurrection in Burkina Faso led to the fall of Blaise Compaoré, president and leader of the ruling party. Due to — or parallel to — the political transition, factors of insecurity developed or were amplified, leading to a reconfiguration of the provision of security at two levels. At the central state level began a reflection around the governance model of security and the improvement of the practices of state security forces. At the local level, non-state security initiatives have multiplied. Drawing on insights from the study of local security provision and providers in the town of Tenkodogo, located in the Boulgou province (Centre-East region), and on its wider integration into the national framework and response to insecurity in Burkina Faso, this article raises and investigates three major questions. First, how is the governance of security (co)produced by (state and non-state) actors in a specific local configuration in Burkina Faso? Second, in what ways does this local experience compare with the state’s response to insecurity and with the nationwide expansion of the Koglweogo movement? Finally, what new perspectives can such reflection at the local and national levels offer to overcome the limits of current approaches regarding local security?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".