Bibliographic record
Abstract
Migration studies have long been characterized as an illegitimate field of research in the French social sciences. This results from the strong influence of the so-called ‘republican’ ideology on social sciences, the constant politicization of the subject in the public arena, the maintenance of a number of taboos revolving around the colonial experience, and a history of the concepts (race, ethnicity, minority) that makes their potential use in scientific analysis controversial. This difficulty of reflecting upon the ethnic fact or racial relations contributed to the implementation of a normative framework, which until recently gave priority to the analysis of integration, leaving the content of ‘racial and ethnic studies' little explored in France. This article offers a historical perspective on the way knowledge has been produced in this field. It highlights the ‘doxa’ of the French integration model in social sciences, elaborating on the controversy over the production and use of ethnic categories in statistics, the various taboos revolving around the role of ethnicity in politics, the discussions launched by the emergence of a post-colonial question and the transition from an analysis of racism to the understanding of a system of discriminations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.024 |
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".