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Record W4292712483 · doi:10.1080/00083968.2022.2077785

Échec ou succès? Les stratégies de subversion et la gouvernance municipale au Niger

2022· article· fr· W4292712483 on OpenAlexvenueno aff
Moumouni Goungoubane, Lisa Mueller

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicAfrican Studies and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsSubversionPoliticsPolitical scienceMargin (machine learning)State (computer science)Irrational numberPolitical economyLawSociology

Abstract

fetched live from OpenAlex

Conventional wisdom suggests that state leaders try to direct resources toward members of their own party and away from their opponents. However, such “strategies of subversion” are more complicated in reality: incumbents do not necessarily favor their co-partisans and sometimes even sabotage them politically. The present article tries to make sense of this apparent paradox, arguing that seemingly irrational political strategies are in fact rational. It focuses on Niger, a country where subversion of the municipal authorities is extremely common. Qualitative and quantitative analyses reveal that subversion strategies do not always help the ruling party in urban areas, in terms of either vote margin or public opinion, but they nevertheless insulate rulers from would-be rivals while securing some electoral gains in the countryside.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0070.006
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.282
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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