Bibliographic record
Abstract
Abstract In liberal democracies with religiously diverse populations, it would be surprising and troubling if a judge relied on a religious text or precept to resolve a legal dispute. It would deeply offend principles of religious freedom if individuals were bound by judicial pronouncement to obey the dictates of a faith they do not share. However, some commentators have long claimed that a person's cultural worldview has an impact on the way they interpret laws and facts, and there is some empirical support for this claim. There is thus reason to expect that judges’ worldviews have some effect on their decision-making. I argue that when judges deliberately avoid engaging with their own moral perspectives, they may mask to themselves the impact that such perspectives have on their decisions. The alternative of explicit reference to religious sources in judicial decisions, however, is too problematic for the religious freedom of legal subjects. I argue that judges should instead endeavor to be conscious of the influence their backgrounds have on their decision-making, but suggest that judicial institutions may be resistant to adopting practices that would support such an approach. The article draws on Canadian and American case law to demonstrate its argument but has wider applicability to liberal states.
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 | 0.022 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".