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Record W2797265379 · doi:10.1017/laq.2018.3

EARLY FORMATIVE PERIOD BIRD USE AT PASO DE LA AMADA, MEXICO

2018· article· en· W2797265379 on OpenAlexaff
Katelyn J. Bishop, Thomas A. Wake, Michael Blake

Bibliographic record

VenueLatin American Antiquity · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMesoamericaPrehistoryPeriod (music)GeographyFormative assessmentSubsistence agricultureArchaeologyEthnographyArchaeological recordEthnologyHistorySociologyArt

Abstract

fetched live from OpenAlex

Avifaunal remains from archaeological sites have a largely unrecognized explanatory potential. Archaeological, ethnohistoric, and ethnographic records have shown that, especially in Mesoamerica, birds and their products have served a wide range of utilitarian, decorative, and symbolic purposes. Despite their ability to inform research on many aspects of prehistoric life, avifaunal remains from archaeological contexts remain under-studied. This paper demonstrates how a holistic approach to their analysis—one that explores several types of human-bird interaction—can move beyond studies of subsistence. A previously reported and newly updated avifaunal collection was reanalyzed to shed light on the relationship between the many uses of birds and the establishment of hereditary inequality at Paso de la Amada, an Early Formative period ceremonial center on the Pacific coast of Chiapas, Mexico. Results indicate that Early Formative people used birds as a source of food, feathers, and bone, and that the ritual use of birds was an important component of status display. Even at this early date, birds were symbolically valuable and played a role in ritual performance, suggesting that their later significance in Mesoamerican ritual, religion, and iconography has an antecedent beginning no later than 1700 BC.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

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 studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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