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Record W4238196594 · doi:10.24908/iqurcp.8850

Civilization in 19th Century Latin America: The “Modernization” of Cities and the Use of Prisons as a Form of Racial Control

2018· article· en· W4238196594 on OpenAlexvenueno aff
Daniella Dávila Dávila Aquije

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansModernization theoryCivilizationState (computer science)PrisonPrison reformEugenicsImprisonmentWhite (mutation)SociologyPolitical scienceCriminologyPolitical economyLaw

Abstract

fetched live from OpenAlex

In the mid to late 19th century, Latin American states adopted European ideals of “civilization.” These ideals were foundational for several state projects that looked to “improve” the aesthetics of Latin American major cities, which were modelled after Paris, the epitome and embodiment of modernization. This “civilizing” reform of the cities caused the ghettoization of non-white communities, given that modern cities were conceptualized as white cities. Thus, to “Europeanize” Latin American cities, Indigenous, Black and Asian peoples needed to be contained and displaced. This was achieved through the creation of the prison system, which came to represent a new form of slavery and a state mechanism for the continuous control of racialized communities. This presentation will examine how criminality was socially constructed to justify the imprisonment of a specific “type” (or race) of person, which is evident given the prison demographics of the time. It will also analyze the theories of eugenics which provided justification for the project of civilization, which only served to worsen the social ills

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

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 studies0.0090.029
Scholarly communication0.0050.003
Open science0.0000.005
Research integrity0.0010.002
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 head0.099
GPT teacher head0.366
Teacher spread0.267 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicRace, History, and American SocietyFrench-language works237,207