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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 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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.025
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 source (direct Gemma or distilled Codex), not a consensus.

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

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