Sondages probabilistes et autres créatures mythiques : usages et limites de la calibration des sondages non probabilistes
Bibliographic record
Abstract
Résumé Cette note de recherche vise à offrir une première introduction aux enjeux de la recherche par sondage, en particulier lorsqu’on utilise des données provenant de panels non probabilistes, comme les sondages en ligne. Nous expliquons le concept clé d’ignorabilité, qui aide à comprendre comment les biais de sélection peuvent affecter les résultats, et comment certaines techniques statistiques – comme la post-stratification et le raking – peuvent tenter de les corriger. À l’aide de simulations, nous montrons dans quels contextes ces méthodes peuvent fonctionner, et dans quels cas elles échouent. Les résultats suggèrent que les sondages non probabilistes présentent des limites importantes pour produire des estimations valides, mais qu’il existe aussi des pistes pour en améliorer l’usage, surtout dans le contexte actuel où ces données sont de plus en plus courantes en sciences sociales.
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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 | 0.023 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".