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Record W2521847757 · doi:10.1111/lasr.12237

Judicial Impartiality and Independence in Divided Societies: An Empirical Analysis of the Constitutional Court of Bosnia-Herzegovina

2016· article· en· W2521847757 on OpenAlexfundno aff
Alex Schwartz, Melanie Janelle Murchison

Bibliographic record

VenueLaw & Society Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
FundersQueen's UniversityBritish AcademyQueen's University BelfastLeverhulme Trust
KeywordsImpartialityJudicial independencePolitical scienceLawPoliticsJudicial activismIndependence (probability theory)Dissenting opinionJudicial discretionConstitutional courtLoyaltyJudicial opinionJudicial reviewLaw and economicsConstitutionSociology

Abstract

fetched live from OpenAlex

The role of constitutional courts in deeply divided societies is complicated by the danger that the salient societal cleavages may influence judicial decision-making and, consequently, undermine judicial impartiality and independence. With reference to the decisions of the Constitutional Court of Bosnia-Herzegovina, this article investigates the influence of ethno-national affiliation on judicial behaviour and the extent to which variation in judicial tenure amplifies or dampens that influence. Based on a statistical analysis of an original dataset of the Court's decisions, we find that the judges do in fact divide predictably along ethno-national lines, at least in certain types of cases, and that these divisions cannot be reduced to a residual loyalty to their appointing political parties. Contrary to some theoretical expectations, however, we find that long-term tenure does little to dampen the influence of ethno-national affiliation on judicial behaviour. Moreover, our findings suggest that this influence may actually increase as a judge acclimates to the dynamics of a divided court. We conclude by considering how alternative arrangements for the selection and tenure of judges might help to ameliorate this problem.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.007
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 head0.056
GPT teacher head0.373
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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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