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Record W2125731584

GEOGRAPHICAL DISTANCE AND CULTURAL KNOWLEDGE: WRITING ABOUT CHINA IN NINETEENTH-CENTURY LATIN AMERICA

2015· article· ca· W2125731584 on OpenAlexaff
Rosario Hubert

Bibliographic record

VenueRevistes Científiques de la University of Barcelona (University of Barcelona) · 2015
Typearticle
Languageca
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsTrinity College
Fundersnot available
KeywordsChinaLatin AmericansGeographyHistoryPolitical scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

To what extent is the production of knowledge of foreign cultures affected by geographical distance? This article explores the porous boundaries between ethnography, geography and fiction in the narrative Viaje de Nueva Granada a China y de China a Francia (1860) by the Colombian Nicolás Tanco Armero. A rare document of exchange between antipodal regions of the planet in the nineteenth century, Viaje… combines the language of the coolie trade, tourist guidebooks and journals of pilgrimage, opening a form of writing about China that considers the rhetorical strategies of peripheral epistemologies. This text inquires into the forms of universalism that prevail over local histories in discussions of modernity, and casts fresh light on discourses of orientalism produced from other allegedly exotic geographies. My claim is that Viaje… evidences a form of writing of China where national identity is at the service of a cosmopolitan form of identification. Geographically, imaginatively and ethically, China becomes a figurative region that transcends the Latin American’s point of enunciation and, in turn, redefines the traveler subjectivity in relation to different forms of production of geographic knowledge: cartography, tourism and pilgrimage.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.138
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.012
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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

Explore more

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