Bibliographic record
Abstract
Win the votes, buy the votes, steal the votes, invalidate the votes! There is a lot that can go right – and so much that can go wrong – in a Ukrainian election. From the opening of the campaign through to the final decision on the results, it is a rollercoaster ride for the candidates, the election workers, and the international observers who have travelled from afar to see it all. In What Ukrainian Elections Taught Me about Democracy long-time election observer Jane Cooper recounts her experience monitoring a municipal election in the mid-sized city of Kirovohrad in 2015. Offering a practical framework for exploring the many things that can go right or wrong during an election, at the core of this story is the inspirational struggle of the poll workers at the bottom of the electoral pyramid to keep the election honest. Cooper describes how election results can be manipulated or falsified and how attempts to do so can be frustrated, providing lessons for citizens of every democratic country. The first work written from the perspective of a Canadian international election observer, the book is an accessible and entertaining story that will appeal to election specialists and the ordinary Canadians who work at the polls on election day, as well as readers who want to learn more about the democratic process in present-day Ukraine. The war in Ukraine has shown us just how endangered democracy is. What Ukrainian Elections Taught Me about Democracy is an insider’s view of election monitoring that sheds light on Canada’s support for international democracy.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".