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Record W2913407617 · doi:10.1163/15718069-24011143

The Strengths and Limitations of the Inclusion of Religious Actors in Peace Processes in Northern Ireland and Bosnia and Herzegovina

2019· article· en· W2913407617 on OpenAlexfundno aff
Vladimír Kmec, Gladys Ganiel

Bibliographic record

VenueInternational Negotiation · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
FundersQueen's University
KeywordsNegotiationArgument (complex analysis)Inclusion (mineral)PoliticsPolitical sciencePolitical economyConflict resolutionInternational relationsSociologyModalitiesPower (physics)Environmental ethicsLawGender studiesSocial science

Abstract

fetched live from OpenAlex

Abstract This article uses a comparative approach to analyze the strengths and limitations of the inclusion of religious actors in peace and transition processes. It compares the theoretical frameworks of Bercovitch and Kadayifci-Orellana, and Brewer, demonstrating how the first helps us understand the strengths of religious actors, while the second sheds more light on their limitations. An analysis of the involvement of religious actors in the peace processes in Northern Ireland and Bosnia and Herzegovina supports the argument that religious actors are more likely to contribute to peace when they are excluded from Track One negotiations and are active in other modalities of participation: in wider social peace processes at national or grass-roots levels. Religious actors can contribute to peace processes especially if they choose to exclude themselves from Track One negotiations in order to avoid the pitfalls of becoming too closely associated with political power.

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)0.054
metaresearch head score (Gemma)0.056
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 score0.063
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0060.012
Scholarly communication0.0080.008
Open science0.0030.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.271
Teacher spread0.261 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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