Gazing into the World of Tattoos: An Invitation to Reconsider how we Conceptualize Religious Practices
Bibliographic record
Abstract
Debates around the visibility of religious symbols – including whether and how to regulate them—have been quite vivid in recent years in Canada, and particularly in the province of Quebec. These discussions often focus on minority religious symbols and are based on the premise that symbols can be removed or modified. In fact, Saba Mahmood (2006, 2009) argues that using the term “symbol” precludes de-facto our ability to entertain the possibility that these symbols cannot be removed or modified. Drawing on 15 interviews with religiously tattooed individuals and tattoo artists in Montreal and Toronto, this article explores the practice of religious tattooing. Interestingly, this practice has been overlooked in debates on the regulation of religious symbols, as well as in the scholarly literature covering those debates. In this article, we are interested in thinking about why this is. We also argue that looking at the practice of religious tattooing helps give further credence to Mahmood’s criticism. It broadens our understanding of religious practices, including alerting us to the importance of the idea of ‘lived religion’ in comprehending how these practices can be an essential part of who someone is. While religious tattoos have largely escaped legal regulation, we conclude with a discussion of how they nonetheless remain the object of a regulatory gaze.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.116 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".