Research on Voting Advice Applications: State of the Art and Future Directions
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it