Navigating post‐conflict governance in Yemen: Decentralization, federalism, and the path to stability
Bibliographic record
Abstract
Abstract Motivation Yemen's prolonged conflict has deeply eroded state institutions, exacerbated regional disparities, and intensified calls for political restructuring. As the country inches toward potential peace negotiations, identifying viable post‐conflict governance models is critical. The urgency is compounded by competing visions from various political factions, regional actors, and civil society, making this an essential area of study for both national recovery and regional stability. Purpose This study investigates the potential of three governance frameworks—enhanced decentralization, federalism, and secession—as models for Yemen's post‐conflict reconstruction. It aims to answer the following research questions: (1) What governance arrangements align best with Yemen's political, social, and economic context? (2) How can institutional legitimacy and local autonomy be balanced in a fragile, divided state? (3) What lessons can be drawn from other countries with similar experiences? Approach and Methods Using a qualitative, comparative methodology, this research analyzes academic literature, policy documents, and governance reports. It integrates historical, institutional, and political economic perspectives to assess the feasibility of each governance model. Case studies from Bolivia, Canada, Ethiopia, and Nigeria provide comparative insights to understand how decentralized or federal systems perform under conditions of fragility. Findings Decentralization emerges as a pragmatic but imperfect path forward, hindered by Yemen's history of centralization and weak local institutions. Federalism offers a more structured solution for regional inclusion but risks deepening divisions if poorly implemented. Secession, although politically attractive to some southern groups, presents high risks of economic disruption and international isolation. Policy implications Yemen's future governance must avoid one‐size‐fits‐all models. A hybrid approach—balancing regional autonomy with national cohesion and institutional reform—is most promising. International actors should support this transition by promoting inclusive dialogue, strengthening local governance capacity, and ensuring equitable resource management to build sustainable peace.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".