Autoethnography of Holy Death: Belief, Dividuality, and Family in the Study of Santa Muerte
Bibliographic record
Abstract
Through an autoethnographic account that interweaves academic observations, my story of how I came to study Santa Muerte in Mexico and the entangled, emotive tale of Abby, a Santa Muerte devotee whom I grew very close to, I discuss the topic of belief in the ethnography of the occult and the “politics of integration”, derisively referred to “as going native”. I reveal how being an ethnographer of the Mexican female folk saint of death has taught me the necessity of dividuality and embracing belief in both the epistemological worlds of academia and the occult. I argue that slipping fluidly between the realm of science and the cosmos of magic has given me access not only to arcane knowledge and networks of practitioners but also through shared experiences of participatory consciousness with devotees of death during our rituals, proffered unique experiences, and new insights through intersubjectivity and interexperience, allowing me to understand the mystical power of Death Herself.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".