Interrituality as a Means to Perform the Art of Building New Rituals
Bibliographic record
Abstract
If we do not consider religious rituals as given to us from the gods, but designed by humans at certain times and in certain contexts we might also track the art of designing and performing the human practice of rituals. Even if we agree that all rituals are taught and learned, they are not meant to be perceived as products of human imagination. The concept “ritual invention” could thus be seen as an oxymoron. My purpose with this article is to analyse how it is possible to simultaneously invent rituals and refer to them as “tradition”. In order to discuss ritual invention I will make use of Rappaport’s definition of rituals as “the performance of more or less invariant sequences of formal acts and utterances, not entirely encoded by the performers.” By introducing the concept of inter-rituality I will show how a skilful ritual leader manages to avoid confusion by recycling ritual acts that structure the performance into a “true event,” in this case the performance of a Kekunit, a god-parent ritual in a Mi’kmaq reserve in Nova Scotia, Canada. The ritual master’s skill in ritual creativity and design is important, and by using a well-known ritual “bank” to collect acts or performances from, he/she turns the performance into a less risky business.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".