UNDERSTANDING LAW AND RELIGION AS CULTURE: MAKING ROOM FOR MEANING IN THE PUBLIC SPHERE
Bibliographic record
Abstract
The relationship between law and religion in contemporary civil society has been a topic of increasing social interest and importance in Canada in the past many years. We have seen the practices and commitments of religious groups and individuals become highly salient on many issues of public policy, including the nature of the institution of marriage, the content of public education, and the uses of public space, to name just a few. As the vehicle for this discussion, I want to ask a straightforward question: When we listen to our public discourse, what is the story that we hear about the relationship between law and religion? How does this topic tend to be spoken about in law and politics – what is our idiom around this issue – and does this story serve us well? Though straightforward, this question has gone all but unanswered in our political and academic discussions. We take for granted our approach to speaking about – and, therefore, our way of thinking about – the relationship between law and religion. In my view, this is most unfortunate because this taken-for-grantedness is the source of our failure to properly understand the critically important relationship between law and religion.
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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.084 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".