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Record W2312531046 · doi:10.2307/4141584

The Manipulation of Human Remains in Moche Society: Delayed Burials, Grave Reopening, and Secondary Offerings of Human Bones on the Peruvian North Coast

2004· article· en· W2312531046 on OpenAlexaff
Jean‐François Millaire

Bibliographic record

VenueLatin American Antiquity · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsIdeologyDestiny (ISS module)Character (mathematics)Human boneHistoryArchaeologyPoliticsBiologyLawPolitical science

Abstract

fetched live from OpenAlex

Abstract A careful reexamination of funerary contexts suggests that Moche (ca. A.D. 100–800) graves were not simply spaces for the disposal of decaying corpses, but contexts periodically revisited by certain members of Moche society. The dynamic nature of funerary practices is documented through an examination of delayed burials. It is argued that these were the product of two distinct ritual processes, one of which involved the storage of corpses to be used as retainers in subsequent rituals. The practice of grave reopening is also explored, leading to the identification of different types of rituals. At least some graves were reopened to remove skeletal parts of possible potent ancestors. Related ideology is addressed by examining cases of bone destruction and the more common secondary offerings of human remains. This study highlights the dynamic nature of Moche mortuary activity while stressing the important role of those in charge of manipulating ancestors’ remains. Finally, it is argued that the Moche shared with their highland neighbors a common vision of the eternal character of human remains, comparable ritual practices involving the human body, and a similar belief in the capacity of the living to influence the course of their destiny through periodic manipulation of ancestors’ remains.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.236
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2004
Admission routes1
Has abstractyes

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