Opinion change and voting behaviour in referendums
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
Abstract Voters in a referendum obtain information and derive voting cues from a variety of sources. Some of these, such as political parties or ideological orientations, are similar to those also found to be influential in elections. Others can be quite different. In some referendums, the issue may be entirely new and unfamiliar to many voters, initiating a ‘learning’ or ‘cue–taking’ process specific to the campaign itself. In referendum campaigns, parties may be internally divided and sometimes send conflicting signals to their electorates. As a result, voting behaviour in referendums often exhibits greater volatility than is found in elections. In the ten papers included in this Special Issue of EJPR , we focus on the process of opinion formation and change which occurred in a number of European, North American and Australia/New Zealand referendums held under a variety of different institutional and political conditions. In this essay, I argue that there are three distinctive patterns of opinion formation and reversal that tend to occur in referendum campaigns, each of which has significant consequences both for voting choice and for referendum outcomes.
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 | 0.006 | 0.002 |
| 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 | 0.001 |
| 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