Quelles statistiques pour analyser les inégalités ?
Bibliographic record
Abstract
La question de la collecte de données permettant de constituer des catégories dites « ethniques » fait l’objet de débats relatifs à la constitution même des catégories et à l’utilisation des données. Au-delà de ces questions, l’impact du type d’analyse statistique utilisée est également à considérer. Cette note de recherche illustre, en utilisant le cas des Premières Nations du Québec, comment la constitution de comparables et le recours à des analyses plus sophistiquées que la simple moyenne permettent de tirer des conclusions différentes susceptibles d’orienter les politiques publiques. Elle montre que ce sont d’abord des facteurs collectifs liés au lieu de résidence qui expliquent l’accès à la scolarité et que c’est cet accès qui est déterminant dans l’accès à l’emploi. Which Statistics to Analyze Inequalities? The Case of Quebec’s First NationsThe question of data collection that allows creating categories known as “ethnic” has been the subject of debates regarding the constitution of the categories as well as the use of data. Beyond these questions, the impact of the type of statistical analysis used needs to be taken into account. This article illustrates, by using the example of Quebec’s First Nations, how the creation of comparable statistics and the use of analyses more sophisticated than the simple measure of the mean allows drawing different conclusions that are likely to guide public policies. They show that it’s first and foremost the collective factors linked to location that explains access to education and that it’s that access that is critical in access to employment. ¿Que estadísticas utilizar para analizar las desigualdades? El caso de las naciones primigenias en CanadáEl problema de la obtención de datos que permiten constituir categorías de tipo « étnico », es objeto de debates sobre el fundamento mismo de las categorías y sobre su utilización. Más allá de un simple replanteamiento, el impacto del tipo de análisis estadístico utilizado debe ser también cuestionado. El caso de las naciones primigenias en Quebec (Canadá) ilustra como la elaboración de elementos comparativos y la utilización de análisis más sofisticados que los que se utilizan habitualmente, permiten obtener conclusiones diferentes susceptibles de orientar las políticas públicas. En este artículo se expone que son en primer lugar los factores colectivos ligados al lugar donde se vive los que explican la escolarización y que esta es determinante para encontrar más tarde un empleo.
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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 | 0.108 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".