Bibliographic record
Abstract
An insistent focus on extremism and radicalization with regard to current Islamist trends masks the failures of pluralist citizenship, amid a larger crisis of identity. Whether in Muslim-majority societies or in the Euro-North American diaspora, “Islam” and “politics” are touted as explaining patterns of severe violence by state/non-state actors. Neither category accounts more than superficially for the complexities at hand, which revolve around exclusionary models of identity, faith and civil society. Successful narratives of inclusive citizenship depend on key markers outside of modernist secular orthodoxy. Theologies of inclusion are vital in fostering pluralist civic identities, mindful of the ascendance of puritanical-legalist theologies of exclusion as a salient facet of public cultures. Multiple surveys reveal the depth of exclusivist conservatism in diverse Muslim societies. These stances not only undermine civil society as a locus for engendering pluralist identities, but also undergird the militant trends that dominate the headlines. Targeting militants is often essential—yet is frequently accompanied by the willful alienation of Muslim citizens even within liberal democracies, and a growing “official” sectarianism among Muslim-majority polities. Convergent pluralisms of faith and civic identity are a vital antidote to the fog that obscures the roots as well as the implications of today’s extremist trends.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".