Catching the Big Wave: Public Opinion Polls and Bandwagons in US and Canadian Elections
Bibliographic record
Abstract
For as long as public opinions have generally thought to be known there have been claims made that knowledge of where people stand can impact both the attitudes and behaviors of others.Previous research has had mixed results in identifying and measuring the effects of -bandwagons‖.This research uses better data and derives tests from contemporary theories of public opinion to show that not only do bandwagons definitively exist, but also that they exist most often among the groups of people we would expect to be influenced by ambient information: those adequately prepared to receive a message but not so sophisticated as to not be influenced by it.This research examines and finds bandwagon effects in four elections total in two different countries (Canada in 2004 and 2006 and the United States in 2000 and 2004) and as such, contributes to the larger scientific endeavor of generalization through comparison.iii Dedication For my patient and loving wife Carolina Results -Favorability -Canada 2006 ........
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".