Bibliographic record
Abstract
Schirin Amir-Moazami's book, Interrogating Muslims, is a brilliant analysis of the techniques of power deployed by the German state in its national project of the "integration" of Muslim citizens.It is an ontological critique that plays on the idea of interrogation indicated in the title.It refers, on one hand, to the superficial question that creates an automatic Muslims/West binary of why don't/can't Muslims "integrate" into Western society, referring to the German state's interrogation of Muslims as not-good-enough-citizens in this discourse.On the other hand, it refers to the author's interrogation of the terms of this "integration".As she notes, Amir-Moazami is not interested in answering the first question.She is interested in why that question is asked in the first place and what the conditions are that make that possible.In answer, she lays out a detailed genealogical analysis of how the terms of the liberal-secular matrix are constructed such that Muslims are constrained as subjects who are and always will be separate and subordinate, perpetually unable to meet the demands placed upon them to perform as "good" citizens.Amir-Moazami's critique of the positivist and Eurocentric logics and methodologies that animate the German state's desire to "measure Muslimness" through "integration" resonates with the intellectual approach of Critical Muslim Studies in this journal ( Editorial Board 2015).Coming from different disciplines, the four contributors to Exchanges,
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".