Σφάλματα δημοσκοπήσεων: αιτίες και αντιμετώπιση
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
This thesis explores the recurring phenomenon of polling inaccuracies, or "polling misses," in election forecasting, examining the systemic, methodological, and behavioral factors contributing to these failures. It begins by establishing the historical context and evolution of election polling, highlighting its critical role in modern democratic processes, media narratives, and campaign strategies. Despite significant advancements in survey technology—from telephone-based to digital and multi-mode platforms—recent elections such as the 2016 U.S. presidential election, the Brexit referendum, and the 2018 Quebec provincial vote have demonstrated notable inaccuracies that challenge the reliability and legitimacy of polls. Central to the analysis is an investigation of the structural vulnerabilities inherent in polling methods, including sampling errors, nonresponse bias, coverage gaps, and inadequate weighting procedures. It underscores the challenges posed by rapidly evolving communication habits and demographic shifts, illustrating how these factors systematically exclude or misrepresent key voter segments, thus skewing poll results. Additionally, the thesis identifies psychological phenomena such as social desirability bias, the "shy voter" effect, late-decider volatility, and the "bandwagon effect," emphasizing their roles in distorting polling accuracy. Through detailed case studies—including notable polling failures in the United States, the United Kingdom, Quebec, and Australia—the thesis demonstrates that polling misses rarely result from isolated errors but rather from a complex interplay of methodological shortcomings and dynamic voter behaviors. It critically assesses contemporary methodological innovations designed to mitigate these errors, such as Multilevel Regression with Post-stratification (MRP), hybrid sampling designs, adaptive fieldwork, and real-time weighting adjustments. The research ultimately advocates for a dual approach: continual methodological refinement paired with heightened transparency and ethical standards. By integrating rigorous statistical techniques with an understanding of voter psychology and behavior, pollsters can better navigate the complexities of modern electorates. This thesis contributes valuable insights and recommendations aimed at enhancing the accuracy, credibility, and utility of public opinion polling, ensuring it remains a vital and trusted component of democratic discourse and decision-making.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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