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Record W2978361510 · doi:10.1177/1542316619871924

Understanding Peacebuilding Coordination and Impact Using a Complex Adaptive Systems Method

2019· article· en· W2978361510 on OpenAlexaff
Hrach Gregorian, Lara Olson, Brian Woodward

Bibliographic record

VenueJournal of Peacebuilding & Development · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsUniversity of Calgary
FundersUnited States Institute of Peace
KeywordsPeacebuildingPsychological interventionUnintended consequencesPolitical scienceComplex adaptive systemInternational relationsPeacekeepingField (mathematics)Management scienceProcess managementComputer sciencePublic administrationBusinessEngineeringPsychologyLawArtificial intelligence

Abstract

fetched live from OpenAlex

The application of complex adaptive systems (CAS) analysis can enhance the effectiveness of coordination in international peacebuilding interventions. This study demonstrates the utility of an inductive CAS analysis approach using rich field data from Kosovo in 2010. It reveals unintended patterns of interaction across key sectors that blocked many intervenors’ peacebuilding policies. Most notably, it shows that international actors pragmatically using informal coordination practices to advance peacebuilding goals also fuelled negative dynamics that, paradoxically, undermined those same goals. The methodology employed illuminates the complex, non-linear dynamics of interactions between international and local actors that led to many hybrid outcomes in Kosovo. Although the resulting insights on Kosovo’s challenges are specific to the 2010 period, many continue to resonate today. More broadly, it shows how a CAS approach can be used to support evidence-based coordination and adaptive management processes in international peacebuilding interventions to improve outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.190
GPT teacher head0.400
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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