The Voter Experience Around the World: A Human Reflexivity Approach
Bibliographic record
Abstract
The experiences that voters have of elections are pivotal in the democratic experience of citizens.However, there has been relatively few multidirectional theorisations of the nature of this experience and the implications.This article reviews existing canonical approaches to understanding the voter experience which are informed by rational choice theory, behaviouralism and constructivism.It offers an alternative human reflexivity approach which anchors the voter experience in structure-agency relationships using realist social theory.The voter experience is defined as the simultaneous process of gathering and responding to knowledge, perceptions and emotions about the electoral process through observing and (non)participating in electoral activities.The citizen is reflexively situated in this experience and is involved in a process of interpreting, re-interpreting, and responding to stimuli, structures and other actors.Using crossnational data, the article identifies the overall global characteristics of the voter experience around the world.Older and more educated voters tend to have a more positive voter experience.Poor voter experiences are also found to lead to citizens 'checking out' of future elections or disengaging from the voting process.The article concludes by setting out the research agenda that arises from the new framework which the special issue takes forward.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".