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Record W4387781622 · doi:10.17151/culdr.2023.28.35.2

Sabiduría y ritual de los hongos sagrados en Mesoamérica

2023· article· es· W4387781622 on OpenAlexaff
Osiris Sinuhé González Romero

Bibliographic record

VenueCultura y Droga · 2023
Typearticle
Languagees
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Esta investigación ha examinado la evidencia histórica y arqueológica sobre los usos rituales de los hongos sagrados en Mesoamérica, específicamente en las antiguas culturas maya, mixteca y mexica, con base en el análisis de fuentes primarias como: códices, esculturas y manuscritos coloniales para lograr un mejor entendimiento de la cosmovisión de los pueblos originarios. Se trata de un estudio interdisciplinario que ha requerido la implementación de diversas metodologías de investigación como: la iconografía, la historiografía, la etnografía y la hermenéutica. Esta investigación se ha fundamentado en una interpretación filosófica de los rasgos culturales que permiten concebir a los hongos como entidades sagradas personificadas, es decir, como seres con voluntad con los que es posible comunicarse, o bien como un medio para contactar con las divinidades; pero también como una fuente de conocimiento y sabiduría. Uno de los objetivos específicos ha sido tomar en consideración el pluralismo ontológico que subyace en las filosofías indígenas para comprender mejor el conocimiento de la naturaleza y el simbolismo asociado a los rituales con hongos psicoactivos. Este artículo forma parte de una investigación más amplia cuyo objetivo es analizar desde una perspectiva intercultural, los usos sagrados, terapéuticos y filosóficos de los hongos con propiedades psicodélicas.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.374
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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