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Record W4308972630 · doi:10.1111/1758-5899.13157

The organisational dimension of executive authority in the Global South: Insights from the <scp>AU</scp> and <scp>ECOWAS</scp> commissions

2022· article· en· W4308972630 on OpenAlexaff
Jarle Trondal, Thomas Kwasi Tieku, Stefan Gänzle

Bibliographic record

VenueGlobal Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsThe King's UniversityWestern University
FundersLudwig-Maximilians-Universität München
KeywordsAutonomyDimension (graph theory)Political scienceEuropean unionExecutive directorPublic administrationAdministration (probate law)BusinessEconomicsInternational tradeManagementLaw

Abstract

fetched live from OpenAlex

Abstract The growing importance of executive authority at the international level has fuelled scholarly debate about the level of autonomy enjoyed by international public administration (IPA), that is, the executive arms of international organisations. Insights from IPAs in the West or Global North, such as the European Union, have largely shaped these debates, whereas data from IPAs in the Global South are largely missing in the discussion. This article seeks to remedy this imbalance and contribute to an organisational‐theory‐inspired conceptualisation of IPA autonomy: We draw insights from survey data from the commissions of the African Union (AU) and the Economic Community of West African States (ECOWAS). We demonstrate that, although both commissions are embedded in inter‐governmental organisations, they demonstrate remarkably strong features of actor‐level autonomy. Thus, this study suggests that even IPAs constrained by an inter‐governmental environment may still wield some degree of autonomy. Finally, the article draws practical implications for reforming IPAs.

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)0.007
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.014
Scholarly communication0.0060.005
Open science0.0000.004
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 head0.039
GPT teacher head0.358
Teacher spread0.320 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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