Bibliographic record
Abstract
Despite their pervasive presence in public discourse, electoral surveys remain enigmatic and provoke skepticism. The future utility of these surveys hinges on addressing this skepticism through pedagogical efforts that highlight both their capabilities and limitations. This challenge is exacerbated by their frequent misuse by the media and political entities, alongside the transformative impact of emerging technologies. While the digital revolution offers advantages, such as expanded accessibility, it also introduces challenges, notably evident biases among online panel participants. Nevertheless, Internet-based surveys are positioned as the future, though ensuring inclusivity and diversity poses a significant challenge. Recent electoral inconsistencies, exemplified by the 2019 State of Mexico elections, underscore this challenge and highlight the credibility crisis confronting surveys. Analogous scenarios in global elections present further obstacles for methodological precision. Meeting these challenges necessitates a nuanced comprehension of survey historical evolution, coupled with an understanding of social dynamics and human behavior, alongside technological and methodological innovations in the social sciences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".