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Record W4392111159 · doi:10.3390/arts13020044

Spanishness and Race in North American Monumental Architecture

2024· article· en· W4392111159 on OpenAlexafffundabout
Lauren Beck

Bibliographic record

VenueArts · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies of Medieval Iberia
Canadian institutionsMount Allison University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRace (biology)ArchitectureHistoryGeographyArchaeologyGeologyPaleontology

Abstract

fetched live from OpenAlex

The representation of Spain, and Spanishness in general, at sites of collective identity in the United States and Canada requires scholarly attention. Many monuments, which range from statues and museums to capitol buildings and national parks, continue to commemorate colonial times despite broader public awareness of the association between colonization and racialized violence, as well as the explicit movement toward decolonization. This commemorative material also demonstrates how non-Spanish settlers have appropriated historical moorings of Spain and its colonial past to reinforce and whitewash their identities in places such as New Mexico and Texas, and even in Newfoundland and Labrador. How monuments are funded and gain public support is another vector that points to the ways that identity—particularly, white identity—informs monumental architecture in ways that exclude people of colour, as well as women, who, when featured in monuments, are usually dehumanized as concepts rather than being the actors of settler-colonialism. This article explores these challenging topics with the aim of articulating a roadmap for future scholarship on this subject.

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: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.012
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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
Published2024
Admission routes3
Has abstractyes

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