Distort, post, repeat: Laundering antisemitism on “cliquey networks” during COVID-19
Bibliographic record
Abstract
Today, in the age of the internet, during recent epidemics such as H1N1, Ebola and Covid-19, it is striking to see how old accusatory scripts are circulated and perpetuated via social media, which serve as new channels for discrimination and blame directed at traditional figures who have been scapegoated at different moments in the history of European epidemics. The article shows how the laundering of information into a cliquey network takes empirical shape during a health crisis. We do so by focusing on VKontakte, a Russian social network similar to Facebook and the 15th largest website in the world in terms of traffic. Using an ethnographic approach to social media, we show how borderline information from an open and easily accessible website is reappropriated, made explicit, and transformed into legally prohibited hate content. It also documents the ability of conspiracy theorists to use the full range of discourse production channels in a country-in this case France-that has very strict laws on hate speech, including that published on social networks. These laws are circumvented by anti-Semitic communities that spread false information in marginal, open and legal networks, thus avoiding legal proceedings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".