Bibliographic record
Abstract
Abstract: History reveals that inter-religious dialogue does not come naturally. Yet it is so crucial to creating a peaceful world. If inter-religious dialogue is to flourish, then participants must be educated for it. At its best, inter-religious education involves an experience of “thin places” that arises from exploring ultimate truths in the presence of the religious other. It also requires the development of “thick religiosity,” that is, knowledge of sacred texts and the play of interpretations; commitment to the communal and transformative character of religion; awareness of the historical and cultural contexts in which the tradition originated and developed; sensitivity to the rich panoply of folk traditions, rituals, art, music, and devotions in which religion has been celebrated and communicated; and attentiveness to the contemplative dimension of life. Thick religiosity, in other words, is a “textured particularism” that forms persons in a religious identity that is simultaneously rooted and adaptive, assured and ambiguous—that is, an identity strong and supple enough to contribute to a responsible pluralism. Conversation between religious others with a thick religiosity is the threshold to thin places.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".