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Record W2207330682 · doi:10.4000/corpusarchivos.1467

José Longinos Martínez: un expedicionario, dos gabinetes de historia natural

2015· article· es· W2207330682 on OpenAlexaff
María Eugenia Ortiz

Bibliographic record

VenueCorpus · 2015
Typearticle
Languagees
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Este artículo tiene por objetivo divulgar cinco textos publicados entre 1790 y 1797 en las ciudades de México, Madrid y Guatemala, acerca del establecimiento de los gabinetes de historia natural conformados en el virreinato novohispano por el expedicionario español José Longinos Martínez. Los textos, que aparecen aquí en orden cronológico, narran los distintos eventos que incidieron en la apertura de los gabinetes novohispano y guatemalteco, y dan cuenta de las contingencias acontecidas en uno y otro evento. Su importancia radica en que, al anunciar y evidenciar el quehacer de Martínez como naturalista, coleccionista y precursor en la formación de gabinetes de historia natural en América, las publicaciones demuestran cómo la práctica europea de coleccionismo de naturaleza fue popularizada en Nueva España tras la llegada de los miembros de la Expedición Botánica y la ejecución de una de sus misiones: colectar y remitir ejemplares naturales útiles al Real Gabinete de Historia Natural en Madrid. Para mostrar esta historia, el análisis de las fuentes se hace observando la faceta de Longinos Martínez como coleccionista, o bien, como protagonista de sucesos relevantes en la historia del coleccionismo americano. Si bien su obra como naturalista y expedicionario es inherente a lo anterior, esto se pone en un plano secundario en la discusión que aquí se plantea.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.281
Teacher spread0.262 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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