Bibliographic record
Abstract
Abstract The twenty-first century has seen an important transition in survey modes used for electoral polls. This transition has not ended yet. It is thus possible to examine differences between modes used in the same election. Different modes are more or less prone to social desirability and use different sampling frames and recruitment strategies that may lead to differences in estimation. However, available literature does not show systematic and substantial differences between modes. In this article, we examine differences between modes across 15 elections and referendums that took place since 2005 in four countries: Canada, France, the United Kingdom, and the United States. We first assess differences in average estimates, variance, trends, and forecasts. We then pool the data to analyze whether there are differences that apply in all contexts. We conclude that differences between modes vary with context and over time. There are some consistent differences, however, as online polls are less likely to detect movement than are telephone or IVR polls. In a context in which online polls are becoming dominant, citizens may not be provided with a reliable portrait of the state of public opinion. IVR polls tended to be more precise than other polls recently, but they also tended to have a conservative bias. For the future, it will be important to monitor closely new developments in the methodology used for election polls. The presence of multiple modes in pre-election polling and new developments in mixed modes would be beneficial to voters and researchers alike.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.178 | 0.068 |
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".