Research on Voting Advice Applications: State of the Art and Future Directions
Bibliographic record
Abstract
Voting Advice Applications (VAAs) have experienced a great deal of success over the past decade, and are now used in many countries around the world. This editorial introduces a Special Issue resulting from a section of the 2015 European Consortium for Political Research (ECPR) conference in Montreal, organized by the ECPR's official VAA Research Network. It discusses the global spread and the popularity of these tools, addresses the history and different branches of VAA research, the current state of the art, and the remaining puzzles in the field. It also focuses attention on the wealth of research that is examining the effects of VAAs on political parties, candidates, and voters, as well as how VAA design choices affect the advice given to voters and their subsequent voting behavior. We hope this Special Issue will also highlight the potential of VAA-generated data for studying party positioning over time and across countries, allowing for comparative analyses of the characteristics and development of parties and party systems.
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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 | 0.057 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".