Rethinking Cultural Keystone Practices: Conflict Resolution Practices as Examples of Salience and Well-Being
Bibliographic record
Abstract
We explore and expand on the concept of Cultural Keystone Practices as an extension of the concept of Cultural Keystone Species and Places. These concepts have helped raise awareness of traditional human-environment interactions by focusing on community well-being and salience. We discuss several keystone-related terms and link them as interdependent for well-being, where salience itself might fall within one keystone concept or another. We focus on three illustrative examples of Cultural Keystone Practices (tribunal de les aigües, Potlatch and dônga) which share conflict resolution as a well-being function. In these three cases, the salience resides in the practice itself, rather than in a particular place or species. Moreover, since the societies that host these practices perceive them as traditions that are not easily substituted with other ‘functional’ equivalents, we can consider them as keystones. Furthermore, we emphasize the need for an assessment strategy for these practices and highlight the limitations of other approaches for the direct and indirect protection of cultural practices, such as UNESCO’s Intangible Cultural Heritage (ICH). Cultural Keystone Practices can be a key enabler for people’s recognition of culture as essential for well-being. A standardized, cross-cultural, community-driven measurement of Cultural Keystone Practices has the potential to serve as a foundation for evaluating the risk of cultural loss associated with significant cultural practices, as well as the consequences of such loss, across diverse contexts.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".