Bibliographic record
Abstract
Abstract In November 2020, as part of Operation Luxor, the home of Austrian Islamophobia scholar Farid Hafez was raided. In December 2023, I was made to withdraw from an almost-complete hiring process at the University of Vienna due to implicit antisemitism accusations. This article compares the two cases to highlight Austria’s uneven landscapes of censorship. In the German-speaking world and beyond, equalizing “anti-racism” or “anti-discrimination” frameworks can obscure crucial power differentials. Whereas an incitement to discourse propels forward antisemitism accusations (especially since October 7), mechanisms of silencing and repression constrain talk of Islamophobia. Simply put, one can easily lose one’s job over accusations of antisemitism but is far less likely to lose one’s job over the accusation of being Islamophobic. What is parallel to antisemitism and fought with equal vigor (and with equal or even more state and institutional support) is not Islamophobia but “Islamism.”
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".