Implications of Lived and Packaged Religions for Intercultural Dialogue to Reduce Conflict and Terror
Bibliographic record
Abstract
Abstract The use of intercultural dialogue (ICD) to promote intergroup understanding and respect is considered as a key to reduce tensions and the likelihood of conflict. This paper argues that understanding the differences among religions – those between packaged and lived religion – enhances the chances of success and makes the effort more challenging. Religions contained and packaged are found in formally organised expressions of religion – churches, denominations, synagogues, mosques, temples and so on. For packaged religions, religious identity is singular and adherents are expected to identify with only one religion and are assumed to accept the whole package of that religion. ICD in this context involves communicating with religious groups such as organisations and encouraging different leaders to speak with each other resulting in platforms filled with ‘heads of faith’ – bishops muftis, ayatollahs, chief rabbis, swamis and so on. In contrast, lived religions involve ritual practices engaged in by individuals and small groups, creation of shrines and sacred spaces, discussing the nature of life, sharing ethical concerns, going on pilgrimages and taking actions to celebrate and sustain hope.There is some evidence that, although packaged religions are declining, lived religions continue at persistent levels. Violent extremism is more likely to be associated with lived rather than packaged forms of religion, making a more balanced intercultural competences approach to ICD critical to countering conflict.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".