Reporting of Public Opinion Polls: Results of Local Polling published in the Windsor Star
Bibliographic record
Abstract
There remains debate on whether polls published in the media during election campaigns impacts on voter choice. Researchers have found limited evidence of a bandwagon, i.e., voters who shift allegiances to support the party perceived as being in the lead, and even less evidence that polls help the underdog. Problems with these studies have been methodological as they often try to measure conversions (which are relatively rare) or that they only look at the exit polls and do not measure campaign dynamics. This study focuses instead on whether polls can affect the learning component of public opinion and in so doing, whether it changes people’s perceptions of the who might win a local race. Windsor area residents were surveyed during the 2004 federal election campaign in part to test whether local poll results during the campaign reported in the Windsor Star changed the public’s perception of the front runner. We found mixed support for the
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".