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Record W2100496899 · doi:10.7202/1020690ar

Latin American Cities in the Eighteenth Century: A Sketch

2013· article· en· W2100496899 on OpenAlexvenueno aff
Woodrow Borah

Bibliographic record

VenueUrban History Review · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Architecture Studies
Canadian institutionsnot available
FundersUniversity of California, Los AngelesCurtin University of TechnologySyracuse UniversityAmerican Philosophical Society
KeywordsSketchSettlement (finance)Latin AmericansEconomyPopulationGeographyHuman settlementEconomic historyPolitical scienceEconomic growthHistoryArchaeologySociologyBusinessEconomicsLawDemography

Abstract

fetched live from OpenAlex

In Latin America the eighteenth century was a time of approximate doubling of the population and considerable economic development and reorientation of the economy. Urban settlement reflected these changes. The bulk of urban growth was by replication of existing patterns into areas of new settlement. Some expansion of older cities and heightening of urban functions took place. In the reordering of regional economies, Buenos Aires, Havana, and Rio de Janeiro profited; Lima failed to prosper. Within existing and new cities, much building replaced older structures in more durable materials, and, in the largest, multi-family, multi-storied structures appeared. Following developments in Europe, beginnings were made in paving streets, providing lighting, installing drains, and so-forth. In similar wise, administration adopted new forms and social welfare was reorganized for more efficient response to natural disasters. Cultural models, copied from Europe, even included the beginning of cafés.

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 score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0040.006
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.033
GPT teacher head0.208
Teacher spread0.174 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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