MétaCan
Menu
Back to cohort
Record W3088389671 · doi:10.1177/1542316620945681

When Institutionalisation Threatens Peacebuilding: The Case of Kenya’s Infrastructure for Peace

2020· article· en· W3088389671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Peacebuilding & Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research CentreUniversity of Ottawa
KeywordsPeacebuildingInstitutionalisationPolitical scienceAgency (philosophy)SustainabilityPublic administrationSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

What are the effects of institutionalisation on long-term peacebuilding? In theory, institutionalisation enhances national and local capacities to sustain peace in the long term. However, in the case of Kenya, institutionalisation now poses a threat to peacebuilding. Institutionalisation is the process of formalising peacebuilding through state policy and structures that aim to sustain more permanent capacities for peace. Institutionalising peacebuilding through the infrastructure for peace in Kenya has increased national capacities for peace. Yet the process of institutionalisation now threatens local agency, effective peace practice, and resource sustainability. These findings are based on qualitative data gathered through semi-structured interviews, participant observation, and documentary evidence. While infrastructures for peace vary in composition and degree of institutionalisation, the findings from Kenya offer insights on the potential threats of institutionalisation to the sustainability of long-term peacebuilding.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.325
Teacher spread0.276 · 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