The ‘headrag tax’: impossible laws and their symbolic and material consequences
Bibliographic record
Abstract
Geert Wilders, the Dutch anti-immigrant politician, whose party received 15.5% of the vote in the elections of June 2010, made his name in part by focusing on the headscarf as an emblem of the Islamic threat in Europe. On 16 September 2009, he (in)famously proposed to levy a ‘headrag tax’ on women wearing a headscarf – a levy for their pollution of the public space. Connecting Islam to gender inequality, he further suggested that the €1000 tax would be donated to women's shelters. The ‘headrag tax’ is an example of what I call an impossible law – a law that would never pass parliament or stand up in a constitutional court. Yet, Wilders' reliance on such proposals shows how these impossible laws can have the power to shape public debate regarding the place of Islam in contemporary immigrant-receiving societies such as the Netherlands. I analyze the parliamentary debate during which Wilders made this proposal and the media responses to it, situating the discussion in larger debates regarding head covering and immigrant integration that have unfolded in the Netherlands over the past decade. I analyze the legal, symbolic and material discourses structuring these debates to show how they delineate the meaning of Dutch national belonging.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".