Schirin Amir-Moazami on the Epistemic Violence of Integration Politics in the Liberal-Secular Matrix
Bibliographic record
Abstract
In Interrogating Muslims: The Liberal-Secular Matrix of Integration ( 2022), Schirin Amir-Moazami brilliantly upends the continuing salience and pervasiveness of the so-called "Muslim Question" (MQ) in the North Atlantic world, with attention to its attending support for liberalism and secularism.Centered on contemporary Germany, over seven chapters, Amir-Moazami asks three primary questions: What is at stake with the imagined-as-urgent governmental work of integration, which Amir-Moazami shows works to manage religious diversity?What is expected of minorities in "successful" integration?And, why are minorities recurringly framed by a narrow and pejoratively framed Islam?In responding to these questions, Amir-Moazami de-naturalizes integration as a good or neutral goal of governance.On the contrary, she shows how the MQ obscures a contested terrain of majority/minority relations and identities.Given that I have few critiques of this very smart book, in what follows I outline what I see as its contributions and how I have recently mobilized its insights in my own work.Amir-Moazami engages and interrogates us/them binaries that undergird a liberal-secular matrix laden in the MQ.In her words, "The speech act that turns Muslims into potentially problematic and testable objects simultaneously constitutes a community of civilized and loyal citizens, who are paradoxically both abstract (liberal democratic) and concrete (native Germans)" (p.123).These
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".