Bibliographic record
Abstract
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".