Follow the Pollsters: Inaccuracies in Media Coverage of the Horse-race during the 2008 Canadian Election
Bibliographic record
Abstract
Abstract.We identify frequent inaccuracies in journalistic interpretations of the horse race (“who is ahead?”) and of change over time (“who is gaining?”) in poll reports during the Canadian election of 2008. We test two explanations. The “mistaken mindset” hypothesis holds that journalists exaggerate the horse race because they systematically miscalculate the margin of error. The “follow-the-pollster” hypothesis holds that journalists follow the horse-race interpretations that they find in pollsters' reports. We find strong support for the “follow-the-pollster” hypothesis in the data and in interviews with pollsters and journalists and conclude that pollsters' reports should be a key element to consider in any attempt to improve the level of accuracy in media reports of the horse race. Résumé.Les journalistes ont souvent commis des erreurs d'interprétation de la marge d'erreur dans les résultats de sondages pendant la campagne électorale canadienne de 2008. Cela les a conduits à surestimer l'avance du parti gagnant et le changement dans le score d'un parti entre deux sondages successifs. Comment expliquer ces erreurs fréquentes? Une première hypothèse affirme que cette surestimation provient du fait que les journalistes se trompent systématiquement dans le calcul de la marge d'erreur. Selon une deuxième hypothèse, les journalistes connaissent tellement mal la marge d'erreur qu'ils se fient à l'interprétation qu'en font les maisons de sondage. Les données empiriques et les réponses aux questions d'entretiens soutiennent la deuxième hypothèse. Nous en concluons que pour mieux porter fruits, les efforts pour améliorer l'interprétation de la marge d'erreur devraient cibler les maisons de sondage autant que les journalistes.
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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 | 0.016 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".