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 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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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