MétaCan
Menu
Back to cohort
Record W3166710448 · doi:10.46743/1082-7307/2012.1132

Economic Aid and Conflict Transformation in Northern Ireland and the Border Area: Respondents’ Perceptions of Awareness, Fairness, Trust Building, and Sustainability

2012· article· en· W3166710448 on OpenAlexafffund
Peter Karari, Seán Byrne, Olga Skarlato, Kawser Ahmed

Bibliographic record

VenuePeace and Conflict Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Dhaka
KeywordsPeacebuildingDignitySustainabilityConflict transformationPolitical sciencePsychological interventionPoliticsEconomic JusticeEconomic growthSustainable developmentPublic administrationLawPsychologyEconomics

Abstract

fetched live from OpenAlex

Intractable ethnopolitical conflicts emanate from the social, political, cultural, and economic marginalization of some community groups. To address these conflicts, the affected groups are often provided with life changing opportunities to enhance justice, equality, dignity and freedom. In the past, Northern Ireland has been a turbulent sea of violent conflict between Unionists and Nationalists. To address the underlying root causes of the conflict, economic aid through the International Fund for Ireland (IFI) and the European Union (EU) Peace II Fund is aimed at facilitating sustainable peacebuilding, reconciliation and community development. In this study, 95 community group leaders, civil servants, and community development officers in Derry, Belfast and the Border Area were interviewed to explore their perceptions about the impact of economic aid in terms of fairness of the application criteria, awareness of both funds, trust building and sustainability. The findings inform future conflict transformation interventions geared towards sustainable peacebuilding, reconciliation and community development in Northern Ireland.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.025
GPT teacher head0.343
Teacher spread0.318 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venuePeace and Conflict StudiesSame topicReligion, Society, and DevelopmentFrench-language works237,207