Perfect strangers: The social dimension of the law's interaction with religion in Canada
Bibliographic record
Abstract
In this dissertation, I critically examine the way in which the law's interaction with religion is conceptualized and evaluated in the context of legal adjudication in Canada. My analysis unfolds through five chapters that explore different contexts and aspects of the law's encounter with religion. Each chapter addresses cases and draws on secondary literature related to the questions and challenges specific to the context being discussed. Together, the chapters provide a multivalent and rich understanding of the law's interaction with religion, analysing a broad range of philosophical, theoretical and social issues at play in the legal adjudication of religious claims. My analysis shows that there is a social dimension at the heart of the law's interaction with religion. Law and religion participate in common processes of social formation and interaction. Although they are autonomous fields of social life they are also interconnected. I argue that viewing the law's interaction with religion in light of its social dimension enhances our ability to understand and critically evaluate the legal adjudication of religious claims. Accounting for this social dimension in the context of legal analysis enables us to identify and face some of the foundational questions and challenges that arise in the process of adjudicating religious matters in law, many of which often go unnoticed. From here, the differences between law and religion, as well as the tensions arising from their interaction, can be framed in a way that is dialectical and mutually constitutive rather than static and isolating. As a whole, my analysis develops a unique way of thinking and talking about the law's encounter with religion in the context of adjudicating religious claims. I offer in this project a set of terms, concepts and ideas that foster and support an open, reciprocal, and integrative engagement between law and religion. In other words, my project recasts the interaction of law and religion in such a way that these perfect strangers can also be seen as friends.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.056 | 0.042 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".