The Last of the White Men: Central Europe’s White Innocence
Bibliographic record
Abstract
At first sight, Midsommar , a horror film set in Scandinavia, seems to have little to do with Central Europe. The title refers to Sweden's traditional mid-summer festival of music, food, and dance. The pagan, cult-like Hårga community lives apart from other humans and only opens up to visitors every 90 years. This time has arrived. Dani, a woman whose sister killed her parents and herself, seeks some relief by accompanying her boyfriend Christian and his fellow graduate students of anthropology, to travel to Sweden and take part in the event. One-by-one, the students are picked off by the Hårga, unaware of what is happening, until only Dani is left. She ends up approving the murder of her boyfriend by the cult, a way for herself to become a member. The film is made by the American director Ari Aster and was distributed by Amazon Prime. The main actors are an Englishwoman, an American, and a Swede. Other than that, almost all the actors are Hungarians pretending to be Swedes. The location, named as Hålsingland – a real place in Sweden – is actually Hungary. Why Hungary rather than Sweden? Certainly, economics must have played a role in the producers’ choice: lax labour laws, favourable tax treatment. But the work of Anikó Imre, a scholar who has been in the forefront of research on how race and whiteness function in the media produced in Central Europe, suggests that there is much more at play. Focusing on the Netflix series, The Witcher , based on Polish source material and shot mostly in Hungary, Imre notes how Central and Eastern Europe provide a location where the vagaries of racial guilt can be dreamed away, where one can escape race as one of the major problems of contemporary life. Imre refers to Central European nations’ claimed lack of responsibility for racialized, colonial exploitation (see Chapters 7–9 in this book), as a fantasy of ‘white innocence’. The fantasy is eagerly supported by media producers in the West, and especially in America. It induces them to consider Central Europe as the ideal location for plots that are free of the complications of race and the guilt that historical racial exploitation brings with it. Their ‘white innocence’ is an imagined condition of pure, undisturbed, and guiltfree whiteness.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".