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Record W4245595143 · doi:10.1177/0192512119832924

Evaluating electoral management body capacity

2019· article· en· W4245595143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Political Science Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsRoyal Military College of Canada
FundersSocial Sciences and Humanities Research Council of CanadaÅbo AkademiVictoria University of WellingtonAustralian National UniversityUniversity of Victoria
KeywordsComparabilityMeasure (data warehouse)Proxy (statistics)Perspective (graphical)Test (biology)BusinessComputer scienceData mining

Abstract

fetched live from OpenAlex

Electoral management bodies (EMBs) perform many functions crucial to promoting electoral integrity, from registering voters to resolving post-election disputes. The capacity of an EMB to perform its tasks, however, is difficult to measure in cross-national perspective. Data on resources and personnel provide only a partial picture of EMB capacity and expert surveys are limited in their comparability. This article presents a new proxy for measuring EMB capacity. It employs a content analysis of EMB websites in 99 countries to measure the presence of indicators of their major functions. It assesses the measurement validity of this new measure of capacity and conducts a small-scale test to determine whether EMBs that score highly do actively communicate with their citizens. An application of this new measure of EMB capacity demonstrates its importance in predicting overall electoral integrity, indicating its importance for future scholarly and policy research.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.142
GPT teacher head0.497
Teacher spread0.355 · 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