Reading religion through the lessons of legal decisions and reactions to them
Bibliographic record
Abstract
The new diversity is presenting some important challenges for social scientists that require a recalibration of our tools. By new diversity I mean that the religious landscape has changed, prompted first by increased immigration that is bringing greater numbers of people whose religious practices are not confined to the majoritarian religions of the receiving countries (see Vertovec, 2007; Meissner and Vertovec, 2014; for a critique of these ideas, see Crul, 2016). Often this is accompanied by a fear of that ‘other’, who is frequently, although not always, Muslim. Second, majoritarian religions are rapidly transforming, losing members, for example, and experiencing declining attendance and participation in life rituals. At the same time, majoritarian religions are refashioning themselves as culture and heritage. Third, in some countries (especially Canada, Australia, the US and some countries in Latin America) this new diversity also includes a renewed attention to indigenous peoples and their spiritualities. In Canada, which is the country I am most familiar with, this attention is part of an awareness of the need to acknowledge the brutal legacy of colonisation for indigenous peoples. Finally, and related to the second shift, is the growing number of people who self-identify as non-religious. All of these changes are shifts in degree rather than kind, but together they constitute a changing landscape in relation to religion. These transformations are resulting in increasingly complex societies that require trans- and interdisciplinary approaches to understand them. As a scholar who is trained in Sociology and Law (and who is located in a department of Religious Studies) I am interested in how law imagines religion and its position in society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".