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Record W4380235322 · doi:10.1515/9780773585744

Elections in Dangerous Places

2011· book· en· W4380235322 on OpenAlexaboutno aff
David Gillies

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2011
Typebook
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer securityInternet privacyBusinessComputer science

Abstract

fetched live from OpenAlex

From Afghanistan and Iraq to Haiti, Cote d'Ivoire, and Egypt, ill-timed, fraudulent, or poorly managed elections have led to discord, violence, and even regime change. While much of the international community views elections as a critical milestone in the stabilization of war-torn societies, Elections in Dangerous Places shows how flawed elections can act as democracy in reverse and diminish political legitimacy and stable governance. Through a series of frank and incisive case studies of conflicted countries, contributors' chapters challenge the centrality and timing of elections as a key pillar of reconstruction at a war's end. They underline the dangers in rushing elections, compromising principles, and lowering the bar for what constitutes free and fair elections in situations of conflict. The authors also underline the economic cost of elections in uncertain political situations and argue that global taxpayers, who must bear the burden, are justified in questioning the value of ill-timed elections. A candid and important study of political turmoil, Elections in Dangerous Places provides valuable lessons and practical advice on how to better mitigate conflict and violence before, during, and after highly charged elections. Contributors include Thomas S. Axworthy (Walter and Duncan Gordon Foundation), Stephen Brown (University of Ottawa), David Gillies (The North-South Institute, Ottawa), Christian R. Hennemeyer (Bridging the Divide), Lisa Kammerud (International Foundation for Electoral Systems, Washington, DC), Johann Kriegler (Electoral Complaints Commission, Afghanistan and IFES Executive Advisory Council), Marc A. Lemieux (University of Ottawa), Khalid Mustafa Medani (McGill University), Susanne D. Mueller (Visiting Researcher at Boston University's African Studies Center), Ben Reilly (Australian National University and Johns Hopkins University's School of Advanced International Studies), Gerald J. Schmitz (M.A., University of Saskatchewan; PhD, Carleton University), Sara Staino (International IDEA), Vincent Tohbi (graduate, National Administration School, Abidjan, Ivory Coast), Francesc Vendrell (Princeton University), and Eugenia Zorbas (Canadian Department of Foreign Affairs and International Trade).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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