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Building Peace with Conflict Diamonds? Merging Security and Development in Sierra Leone

2009· article· en· W2017801763 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.

Bibliographic record

VenueDevelopment and Change · 2009
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSierra leoneLivelihoodIndustrialisationEconomic growthPolitical scienceEconomicsDevelopment economicsLawGeography

Abstract

fetched live from OpenAlex

ABSTRACT This article examines the merging of security and development agendas in primary commodity sectors, focusing on the case of peace‐building reforms in Sierra Leone's diamond sector. Reformers frequently assume that reforming the diamond sector through industrializing alluvial diamond mining will reduce threats to security and development, thereby contributing to peace building. Our findings, however, suggest that the industrialization of alluvial diamond mining that has taken place in Sierra Leone has not reduced threats to security and development, as it has entailed human rights abuses and impoverishment of local communities without consolidating state fiscal revenues and trust in local authorities. This suggests alternative strategies for resource‐related peace‐building initiatives, which we consider at the end of the article: the decriminalization of informal economic activities; the prioritization of local livelihoods and development needs over central government fiscal priorities and foreign direct investment; and better integration between local economies and industrial resource exploitation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.023
GPT teacher head0.214
Teacher spread0.191 · 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