Why Did the Polls Overestimate Liberal Democrat Support? Sources of Polling Error in the 2010 British General Election
Bibliographic record
Abstract
Pollsters once again found themselves in the firing line in the aftermath of the 2010 British general election. Many critics noted that nearly all pollsters in 2010 expected a substantial surge for the Liberal Democrats that did not materialize. Basing conclusions regarding the relative merits of pollsters or benefits of methodological design features on inspection of just the final poll from each pollster is inherently problematic, because each poll is subject to sampling error. This paper uses a state‐space model of polls from across the course of the 2010 election campaign which allows us to assess the extent to which particular pollsters systematically over‐ or under‐estimate each main party’s share of the vote, while allowing for both the usual margins of error for each poll and changes in public opinion from day‐to‐day. Thus, we can assess the evidence for systematic differences between pollsters’ results according to the use of particular methodologies, and estimate how much of the discrepancy between the final polls and the election outcome is due to methodological differences that are associated with systematic error in the polls. We find robust evidence of an over‐estimation in Liberal Democrat support, but do not find evidence to support the hypothesis that the polls erred due to a late swing away from the party, nor that any of the methodological choices made by pollsters were significantly associated with this over‐estimation.
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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 | 0.076 | 0.300 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".