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Record W2891678657 · doi:10.1163/15718069-23031164

Exploring the Role of Culture in Shaping the Dagbon Ethnopolitical Peace Negotiation Processes

2018· article· en· W2891678657 on OpenAlexaff
Mathias Awonnatey Ateng, Joseph Abazaami, A. Agoswin Musah

Bibliographic record

VenueInternational Negotiation · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNegotiationContext (archaeology)SociologyInternational relationsCultural conflictPolitical sciencePoliticsSocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract Culture is a significant factor in peace negotiation processes. It frames the parties’ perspectives and strategies to managing the conflict. This study explores the role of culture in the peace negotiation processes of the Dagbon ethnopolitical conflict of Northern Ghana. Twelve elders from the Dagbon Traditional Area with an in-depth understanding of the traditions and culture of Dagbon were interviewed using an unstructured interview guide. Similar to most findings on cross-cultural negotiation processes in high-context cultures, all the negotiators were men. The issues negotiated were largely based on the culture and traditions of Dagbon, and the interest and priorities of the negotiators were culturally defined. As with many other ethnopolitical conflicts, the culture of Dagbon was key in shaping the process and outcome of the peace negotiations. It is imperative for cultural issues to be properly understood and addressed satisfactorily in order not to undermine peace negotiation processes.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
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 head0.106
GPT teacher head0.346
Teacher spread0.240 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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