Revisiting the Madrassa Question in Pakistan: Worlding Lived Religion and Religious Education in Urban Spaces
Bibliographic record
Abstract
The association of madrassas as “breeding grounds for terrorists” is problematic, exacerbated by a lack of understanding of how Islamic religious schools function and contribute to cities and urban social life. Our article provides an interpretative examination of the so-called madrassa question by explaining the urban-spatial embeddedness of madrassas and emphasizing the heightened sense and deployment of religious identities in the quotidian “worlding” of “lived religion” and “lived religious education” of research participants in two madrassa communities in Islamabad, Pakistan. Positioned within the growing research on urban sociology and geographies of the intersections of religion and education, this article examines lived religion and religious education within urban spaces. It discusses ethnographic findings on the performance and reproduction of spatially grounded extrareligious roles, identities, and practices in city-based madrassas. We emphasize the religious and nonreligious meanings people attach to these identities and practices, and how these are manifested, represented, and experienced in urban community spaces. We demonstrate madrassas’ connection to people’s place-making practices and meaning-making as historical processes and purposeful action. Urban landscape, quotidian religious practices, and extra-local political economy are important to linking place, human aspirations, and lived religion in reframing the madrassa question in Pakistan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".