The Forging of Institutional Autonomy: A Comparative Study of Electoral Management Commissions in Africa
Bibliographic record
Abstract
Abstract.Building upon the theoretical framework of new-institutionalism, this article concentrates on electoral management commissions (EMCs), which, though theraison d'êtreof political battles in many African countries, have attracted very little analysis in academic literature. The sample includes seven countries and I concentrate on the issue of forging institutional autonomy. I propose five modes of institutional forging that produce different levels of autonomy. At the same time, I argue that autonomy refers less to legal provisions than to the empiricalself-reinforcingandlock-inprocesses, which may or may not take place depending onpower relationsin the political arena. In turn, however, these differences may explain the contrasting trajectories African states have takenvis-à-visdemocratization. Résumé.Alors qu'elles sont au cœur des luttes politiques dans plusieurs pays africains, les commissions électorales sont peu étudiées dans la littérature sur la démocratisation. Cet article utilise un cadre théorique néo-institutionnel pour analyser les commissions de sept pays en se concentrant spécifiquement sur la question de l'autonomie institutionnelle. Il met à jour cinq modes de création institutionnelle correspondant à différents niveaux d'autonomie qui, en retour, expliquent les trajectoires divergentes des pays en matière de démocratisation. L'autonomie est à rechercher moins dans les prérogatives juridiques que dans les rapports de forces entre acteurs au moment de l'émergence de l'institution et dans les processus subséquents d'autoreproduction institutionnelle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".