Bibliographic record
Abstract
Abstract If group-based cross-pressures are key to explaining the surge in electoral volatility, there should be evidence of an over-time increase in levels of cross-pressure too. This chapter provides a test of this intuition. In a first section, the longitudinal election survey data from Australia, Canada, Denmark, Germany, Great-Britain, the Netherlands, Sweden and the United States is used to assess, in a bivariate way, how levels of group-based cross-pressure vary over time. This descriptive analysis suggests much change, especially in countries characterised by an increase in electoral volatility. In a next section, this over-time trend is scrutinized more - with specific attention for evaluating the extent to which this change is driven by generational change or reflects period effects. The third section of this chapter evaluates the role that party-system change plays for the trend toward higher levels of group-based cross-pressure. In a final section, it is verified whether focusing on a main correlate and outcome of group-based cross-pressure, i.e., party ambivalence, similarly shows evidence of over-time change. Overall, the results in this chapter provide much evidence of change. In countries that have been marked by a surge in volatility, there also is evidence of increased levels of group-based cross-pressure. This change appears to affect voters of all generations, and even holds when the increased fragmentation of party systems is accounted for
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".