Changing Preferences for Brexit: Identifying the Groups with Volatile Support for 'Leave'
Bibliographic record
Abstract
This paper explores the dynamics of support for the UK’s departure from the EU over the course of 2016 and the first quarter of 2017. It further identifies groups with a particular profile in terms of political attitudes and behaviours and explores whether these groups show a marked change in their support for leave. The paper draws on two contrasting perspectives on voter volatility. While the first one considers the phenomenon to be a characteristic of whimsical, uninterested and disengaged people, the second one sees it in a more positive light as it associates volatility with the informed and emancipate citizen holding politicians to account. The study uses Waves 6, 7 and 8 of Understanding Society and conducts various analyses, including latent class analysis (LCA), to explore the research questions. LCA yields four groups with distinct political profiles. Only one of these groups, labelled “the highly engaged and satisfied”, shows a significant increase in support for leave. The other groups, including “the non-engaged” and “the dissatisfied”, are not becoming significantly more or less supportive of leave. The results are thus more in accordance with the second perspective.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".