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Record W7128139598

Σφάλματα δημοσκοπήσεων: αιτίες και αντιμετώπιση

2025· article· W7128139598 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOpen MIND · 2025
Typearticle
Language
FieldMathematics
TopicSurvey Sampling and Estimation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPollingLegitimacyContext (archaeology)Transparency (behavior)Presidential systemWeightingSocial mediaSurvey data collection
DOInot available

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.003

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.

Opus teacher head0.212
GPT teacher head0.466
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it