Bibliographic record
Abstract
The ambivalent role of religion in modern citizenship is hardly new, whether in terms of the “hardware” (legal-political institutions and processes) or “software” (public attitudes and habits). Faith narratives can vigorously contest liberal claims of civil belonging and freedom, such as on gender and secularism. Yet liberal citizenship also benefits from justice claims anchored in interpretations of religion, ranging from equality and solidarity to nonviolence and reconciliation. Identity politics is a formative part of liberal citizenship, with a dominant tribal discourse legitimated by the accommodation of minority ethno-religious claims to equity and equality. Nativist populism aggravates an already adversarial relationship between faith and liberal citizenship, notably for minority religions. This paper argues for symbiosis between liberal citizenship and diverse religious identities—a political theology that takes pluralism seriously. While liberalism purports to minimize expectations of individual virtue, civic pluralism calls for the inclusion of collective and individual ethical commitments, beyond ruptures of secular and sacred that shape a jurisprudence on the “burdens of accommodation.” Evidence from Canada, among other liberal settings, suggests that the alternative is civic fragmentation that favours majoritarian tribalism.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".