Bibliographic record
Abstract
Le baromètre racisme est un sondage annuel réalisé depuis 1990, offrant un véritable baromètre sur les opinions à l’égard du racisme et des discriminations en France. Réalisée en face à face du 6 au 14 novembre 2018, l’édition 2018 du baromètre racisme contient un échantillon de 1 007 personnes, représentatif de la population métropolitaine, âgée de 18 ans et plus, constitué d’après la méthode des quotas (sexe, âge, profession du chef de ménage, après stratification par région et catégorie d’agglomération). Comme tous les ans depuis 2000, une analyse détaillée du sondage a été réalisée par une équipe de chercheurs de Sciences Po et du CNRS et, et présentée dans le rapport 2018 de la CNCDH sur la lutte contre le racisme, l’antisémitisme et la xénophobie. The Racism Barometer is an annual survey existing since 1990, which offers a true barometer on opinions regarding racism and discrimination in France. The 2018 edition took place between the 6th and 14th of November 2018. It was realized face to face on a sample of 1 007 people, representative of the metropolitan French population, aged above 18 years, designed with a quota method (sex, age, head of the household’s profession, stratified by region and agglomeration category). Like every wave since 2000, a deep analysis of the survey has been conducted by a research team from Sciences Po and CNRS, and presented in the 2018 report of the CNCDH about racism, antisemitism and xenophobia.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".