Les ressorts du racisme anti-asiatique en France, des manifestations de 2010 au Covid-19 <br>
Bibliographic record
Abstract
En aout 2016, le meurtre d'un couturier chinois a Aubervilliers provoquait des manifestations massives de la communaute asiatique de France. Ce drame avait mis en lumiere les agressions physiques, mais aussi le racisme du quotidien subi par une population jugee discrete, travailleuse et communautariste. Cette mobilisation, faisant echo a celle de 2010, a permis de donner une visibilite associative, mediatique et politique, a une communaute parfois oubliee des luttes anti-racistes des dernieres decennies. Quatre ans plus tard, la pandemie du Covid-19 est venue replacer le racisme anti-asiatique dans le debat public. Aux titres de journaux racoleurs, se sont ajoutes des vexations du quotidien mais aussi des appels a frapper chaque Chinois sur les reseaux sociaux. Cette conference aura pour but d'analyser les specificites du racisme anti-asiatique en France et de cerner ses racines historiques, politiques et sociales. Nos intervenants, jeunes associatifs, journalistes et chercheurs, debattront egalement des moyens d'action pour faire avancer la lutte contre le racisme et les prejuges anti-asiatiques.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".