La Méthodologie des sondages électoraux de l'élection présidentielle française de 2007, chroniques d'un problème récurrent
Bibliographic record
Abstract
Election Poli Methodology and the 2007 French Presidential Elections - Chronicle of a Recurring Problem: The 2002 presidential élection polls will be remembered for a long time because their error in predictions had dramatic consequences. What influence did those elections have on French opinion poli research and was it able to revise its methods? Was it able to improve its voting estimates? Research has shown that French polis are now more able to distinguish between voting for the left and the right than in 2002. However, French polls have not improved their capability to estimate intentions to vote for major candidates. The discrepancies between means for twelve polis published during the last week before voting vary from 1.8 points for Royal to 3.2 points for Sarkozy and 3.8 points for Le Pen. These results show that the estimation error for Le Pen is the same in 2007 as in 2002, but in the opposite direction. As in 2002, samples were biased concerning level of education - strong under-representation of the less educated - as well as for voting profile - strong non-declaration of voting for Le Pen. One can thus conclude that the problem of estimating the extreme right vote remains in its entirety and that polling institute methods have not changed greatly. Cooperation between the institutes and researchers should permit further explanations and possible solutions.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".