UN Stabilisation Operations and the Problem of Non-Linear Change: A Relational Approach to Intervening in Governance Ecosystems
Bibliographic record
Abstract
In recent years, the United Nations (UN) has increasingly turned towards stabilisation logics in its peace operations, focusing on the extension of state authority in fragile, conflict-prone areas. However, this concept of stabilisation relies upon a series of binaries — formal/informal actors, licit/illicit activities, governed/ungoverned space — which often distort the far more complex power relations in conflict settings. As a result, UN peace operations tend to direct resources towards state institutions and ignore a wide range of non-state entities, many of which are crucial sources of governance and exist at the local and national level. In response, this article places the UN’s stabilisation approach within a recent trend in peace research focused on the hybrid nature of socio-political order in conflict-affected regions, where non-state forms of governance often have significant and legitimate roles. Rather than replicate misleading state/non-state binaries, the article proposes a relational approach and develops a novel analytical framework for analysing a wide range of governance actors in terms of different forms of symbiotic relationships. It then applies this approach to specific examples in Mali and the Democratic Republic of the Congo (DRC), demonstrating the highly networked power arrangements present in conflict settings. The article posits that a relational approach would avoid many of the false assumptions at the heart of today’s stabilisation interventions and would instead allow the UN to design more effective, realistic strategies for pursuing sustainable peace in modern conflict settings. It concludes that relationality could be used more generally, including to explain the waning potency of the so-called ‘third wave’ of democratisation.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".