MétaCan
Menu
Back to cohort
Record W2026507234 · doi:10.1177/1356389009360471

Peace and Conflict Impact Assessment (PCIA) in Community Development: A Case Study from Mozambique

2010· article· en· W2026507234 on OpenAlex
Lisa Bornstein

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.

Bibliographic record

VenueEvaluation · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsMcGill University
Fundersnot available
KeywordsSituational ethicsGovernment (linguistics)Work (physics)Quality (philosophy)Public relationsPolitical sciencePublic administrationSociologyBusinessEngineeringLaw

Abstract

fetched live from OpenAlex

Peace and conflict impact assessment (PCIA) is a tool that potentially can improve the quality of development work in conflict zones. PCIA’s conceptual strengths and weaknesses are much debated but few studies to date have examined its use in practice. For this article, PCIA was used to structure research on conflict and peace dynamics in post-war Mozambique. The findings address both local peace-building outcomes and the usefulness of PCIA. PCIA functioned well as a tool for situational analysis, richly documenting sources of conflicts, competing claims over resources and rights, and problematic policies on the part of development organisations, government and private actors. Difficulties associated with the gathering of information stemmed from systemic power differentials between ‘researchers’ and ‘respondents’, and intensive demands on time and resources. The article concludes that PCIA, if used flexibly and in dialogue with local people, could prove a valuable complement to existing assessment tools in conflict areas.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.404
Teacher spread0.356 · 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