Bibliographic record
Abstract
With pressure on Twitter from governments and others intent on preventing the spread of racist, including anti-Semitic and Islamophobic, messages, it appears that some right-wing populist users have moved to Gab, where they are less likely to be censored. There appears to be a split between those who are willing to play by the new rules on Twitter, and those who prefer the ability to express their racism more openly on Gab. We believe that this split reflects a broader, ongoing split within the far right. In the United States and in Europe, the more "moderate" right, hopeful of electoral successes, conserves its Islamophobic and anti-migrant rhetoric, but is moving away from overt anti-Semitism. Meantime the diehard anti-Semites develop their own, separate networks, online and offline, and these may now include Gab. Our test case is what we call the "Soros Myth," which accuses the Hungarian-born, American-Jewish financier George Soros of instigating or supporting an astonishingly large array of causes and events that right-wing populist resent, including the mass migration of Muslim refugees to Europe. The article discusses the methodology of gathering relevant information on Twitter and Gab, and the preliminary results, which strongly support our working hypothesis that when it comes to the Soros Myth, the more overtly anti-Semitic content is now found on Gab.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".