The Voter Experience Around the World: Lessons for Theory and Practice
Bibliographic record
Abstract
This special issue has introduced the human reflexivity approach as a framework for studying elections. Empirical studies in the volume have then considered how institutional design, cultural practices and strategic actions come together to inform the voter experience – and how this experience, in turn, has broader consequences for the quality of elections and democracy. This concluding piece summarises some of the key empirical findings and draws out lessons for policy makers. Given that citizens who are younger and have fewer formal educational qualifications self-report a poorer voter experience, there is an urgent need for action to equalise democracy. The special issue provides empirical evidence in support of implementing automatic and assisted voter registration, civic education, limiting overly restrictive voter identification requirements, caution with concurrent elections and improved transparency practices. A human reflexivity approach, it is argued, gives policy makers greater theoretical freedom to support better elections and democracy – rather than follow ‘rational’ logics of power maximisation both described and prescribed by traditional rational choice theorists.
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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".