AN ASSESSMENT OF THE RELIABILITY OF POLLING DATA AHEAD OF FUTURE PRESIDENTIAL ELECTIONS IN THE UNITED STATES
Bibliographic record
Abstract
The accuracy of polling data has been a subject of intense debate in recent years, particularly in the wake of high-profile misses in the 2016 and 2024 United States of America’s presidential elections despite polls indicating a high likelihood of a Hilary Clinton and Kamala Harris victory, Trump won in both elections. This study provides an assessment of the reliability of polling data ahead of presidential election polls in the United States of America. Contemporary polling faces numerous challenges, including declining response rates, the rise of non-probability internet samples, and increasing concerns about survey error. This study employs a mixed-methods approach, combining quantitative analysis of polling data from the 2016 presidential election to the 2024 presidential election with qualitative insights from expert interviews as both elections provide opportunities to examine the reliability of polling data in a more contemporary context. The quantitative analysis assesses the accuracy of polling data in predicting election outcomes, while the qualitative component explores the challenges and limitations of polling in the contemporary media landscape. The study adopted structural functional theory as developed by sociologists such as Émile Durkheim (1893) and Talcott Parsons (1949), emphasizes the interconnectedness of social structures and institutions in maintaining social order. The study concludes that there is the need for caution when interpreting polling data and underscores the importance of continued research into the challenges facing contemporary polling. The study recommends that the public should be cautious when interpreting polling data and recognize the potential limitations and biases of polling estimates
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How this classification was reachedexpand
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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".