Are all Polls Equal? Analyzing the Polls of the US2020 election, a new Perspective
Bibliographic record
Abstract
At the dawn of the 2024 American presidential campaign, it is not pointless to reassess what happened during the 2020 campaign. In that election, the polls have been the least accurate since 1996, with a notable disparity in results depending on the poll's mode of administration and sampling frame or source. Based on their methodology, the 222 national-level campaign polls were categorized as mixed-mode (16%), single-mode quasi-random polls (25%), and web opt-in polls (59%). Using local regression and multilevel analysis, the study revealed differences in campaign trends across categories. All the polls using random or quasi-random sampling indicated an initial rise in voting intention for Joe Biden followed by a decline until election day, while web opt-in polls showed his support as stable. Notably, mixed-mode polls provided an almost perfect election forecast. The poll estimates of the last ten days support these findings, showing higher accuracy within the mixed-mode and single-mode quasi-random polls compared to web opt-in polls.The study suggests that different modes and sources capture varying segments of the population, leading to more accurate polling. The results stress the need for academia, media, and pollsters to closely monitor the methodological diversification introduced in the 2020 election.
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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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".