The Potosi principle: religious prosociality fosters self-organization of larger communities under extreme natural and economic conditions
Bibliographic record
Abstract
We show how in colonial Potosí (present-day Bolivia) social and political stability was achieved through the self-organization of society through the repetition of religious rituals. Our analysis shows that the population of Potosí develops over the time a series of cycles of rituals and miracles as a response to social upheaval and natural disasters and that these cycles of religious performance become crucial mechanisms of cooperation among different ethnic and religious groups. Our methodology starts with a close reading and annotation of the Historia de Potosí by Bartolomé Arzans. Then, we model the religious cycles of miracles and rituals and store all social and cultural information about the cycles in a multirelational graph database. Finally, we perform graph analysis through traversals queries in order to establish facts concerning social networks, historical evolution of behaviors, types of participation of miraculous characters according to dates, parts of the city, ethnic groups, etc. It is also important to note that the religious activity at the group level gave native communities a way to participate in the social life. It also guaranteed that the city performed its role as producer of silver in the global economic structure of the Spanish empire. This case proves the importance of religion as a mechanism of stability and self-organization in periods of social or political turbulence. The multidisciplinary methodology combining traditional humanistic techniques with graph analysis shows a great potential for other sociological, historical, and literary problems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".