Seeing Past the 'Post-9/11' Framing: The Long Rise of Anti-Islam Politics in the Netherlands
Bibliographic record
Abstract
In the early 2000s, anti-Islam parties rose to unprecedented prominence in the Netherlands.Within the Dutch polity, the parties' mainstream popularity is widely understood as a product of the post-9/11 climate; defined by "Islamist" terrorist attacks throughout Western Europe, and concurrent political discourses on the "crisis" of multiculturalism.Researchers critical of this interpretation have analysed anti-Islam politics in the Netherlands as a product of the post-9/11 security climate.Yet framing anti-Islam politics as 'post-9/11', underestimates the long-term presence of anti-Islam politics and disguises systemic issues of minority discrimination that have long plagued Dutch society.In order to 'see past' the post-9/11 framing, this paper examines the history of anti-Islam politics within the broader historical context of the Dutch 'multicultural myth' and issues of including newcomers into Dutchness since 1945.The curious trend amongst Dutch politicians to circulate anti-Islam politics through independently released books/films is explored and its significance discussed.iii Dedication To my parents, who continue to share with me their eternal delight and curiosity in learning and who teach-through example-the importance of heartfelt hard work and mindful engagement with the social issues that shape our world.And to my dear friend Iefke who in no small terms taught me Dutch, and revealed the untranslatable gezelligheid of the Netherlands, that is worlds away from the subject of this work.A master's thesis is an inherently collaborative endeavour.I would like to thank my supervisor Professor Jennifer Evans for her unwavering support and guidance as this project evolved.Her careful revisions challenged and taught me to write with a clarity that displays confidence in the ideas presented, which is an immeasurable gift.My sincerest thanks also to my secondary reader Professor James Casteel for his generous feedback and our many impromptu discussions of European history that were such a joyous and inspiring part of the research process.A research-travel grant from Carleton's Centre for European Studies (CES) and the European Union made fieldwork for this project possible for which I am very grateful.Thanks also to my magical friends Reiko Obokata
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".