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Record W4393858490 · doi:10.1163/17087384-bja10089

Exploring Attempts at Judicial Resolution of the Nkonya-Alavanyo Communal Conflict in Ghana

2024· article· en· W4393858490 on OpenAlexaffvenue
Prince Duah Agyei, Felix Odartey‐Wellington

Bibliographic record

VenueAfrican Journal of Legal Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsCape Breton University
Fundersnot available
KeywordsConflict resolutionState (computer science)Economic JusticePolitical sciencePrivilege (computing)LawSociologySubject (documents)Transitional justiceResolution (logic)Law and economics

Abstract

fetched live from OpenAlex

Abstract While the literature on the Nkonya-Alavanyo conflict references litigation and its apparent ineffectiveness in resolving the conflict, there is a paucity of detail about this litigation. This paper contributes to a more holistic comprehension of the discourses structuring resolution attempts in this conflict, with lessons for the resolution of communal conflicts generally. Drawing on archival data, media reports, and field interviews, we examine the trajectory of the Nkonya-Alavanyo conflict in the Ghanaian judicial system as an example of an intractable communal conflict that has defied legal attempts at resolution. We argue that judicial attempts at resolving the conflict have been ineffective because the resultant juridical discourse is polysemic and – to the extent that the non-negotiable value of justice is a factor in the conflict – is subject to divergent articulations. Second, we submit that the juridical discourse competes with State, civil society, and partisan articulations that do not privilege judicial decisions, with State discourse increasingly being one of militarisation.

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)0.013
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 score0.013
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.010
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0020.003
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 head0.220
GPT teacher head0.371
Teacher spread0.151 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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