Towards More People-Centric Peace Operations: From ‘Extension of State Authority’ to ‘Strengthening Inclusive State-Society Relations’
Bibliographic record
Abstract
Periods of conflict erode trust between national and local authorities and the people they govern, a trust that needs to be re-established. As peace operations are undertaken by inter-governmental bodies that tend to be inherently state-centric, however, peace operations need to go beyond merely supporting the extension of state-authority and strengthen inclusive state-society relations by supporting and facilitating inclusive processes that can address social cohesion, inequalities and marginalization. In order to support the emergence of resilient societies, these operations must help states and their societies to develop inclusive processes that enable participatory and responsive state institutions that are closely connected to the social institutions in the communities they serve. Furthermore, peace operations are often perceived as being partial to the party in government, while the government of the day often discourages peace operations from engaging with civil society. In the first part of this paper we explain why strengthening inclusive state-society relations is an important issue to address when peace operations are mandated to foster peace processes that can resist relapse. The second half of the paper shifts the attention to how peace operations can enhance and support state-society relations. In addition to monitoring the effect peace operations have on state institutions, operations should monitor how people experience the role and impact of peace operations. This can be assessed in a number of ways, including by involving representative advisory groups from civil society and local communities in assessments, analysis, planning, implementation and evaluation, so as to ensure continuous direct input and feedback from the society on the work of the peace operation.
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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 | 0.040 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".