Bibliographic record
Abstract
The environmental crisis has challenged faith traditions to take a stand and act both globally and locally. Statements and action build on reinterpretations of tradition, which also produce a variety of ritual applications. Environmental rituals, for example, deal with the grief and anxiety caused by environmental crisis or seek to have a concrete impact on local environmental problems. The anthropologist Roy Rappaport (1926–97) examined religious environmental rituals, firstly as a way of regulating ecological balance. Secondly, he saw religiously motivated environmental rituals as a way of changing human thinking and behaviour in an era of environmental crisis. These perspectives can be applied in at least three ways: firstly, by looking at how rituals are used in religious communities that are directly dependent on the natural environment; secondly, by examining how religious communities use rituals in various situations related to environmental issues; and thirdly, by focusing on how Rappaport’s ideas could be used to engage in environmental action. In this article, I focus on religiously motivated environmental rituals and the perspectives that Rappaportian ritual approach provides for examining them. As examples, I use the struggle of the Canadian Mi’kmaq indigenous community over the fate of their sacred mountain and the ordination ritual of Thai monks, who ordain trees under threat of felling in a Buddhist monastic community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".