Religious Diversity in Australia: Rethinking Social Cohesion
Bibliographic record
Abstract
This paper argues for a reconsideration of social cohesion as an analytical concept and a policy goal in response to increasing levels of religious diversity in contemporary Australia. In recent decades, Australian has seen a revitalization of religion, increasing numbers of those who do not identify with a religion (the “nones”), and the growth of religious minorities, including Islam, Buddhism, Hinduism, and Sikhism. These changes are often understood as problematic for social cohesion. In this paper, we review some conceptualizations of social cohesion and religious diversity in Australia, arguing that the concept of social cohesion, despite its initial promise, is ultimately problematic, particularly when it is used to defend privilege. We survey Australian policy responses to religious diversity, noting that these are varied, often piecemeal, and that the hyperdiverse state of Victoria generally has the most sophisticated set of public policies. We conclude with a call for more nuanced and contextualized analyses of religious diversity and social cohesion in Australia. Religious diversity presents both opportunities as well as challenges to social cohesion. Both these aspects need to be considered in the formation of policy responses.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".