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Canadian Architecture in the Nineteenth Century: Identities in Tension

2017· other· en· W4240717986 on OpenAlexaboutno aff
Marc Grignon, Christopher Thomas

Bibliographic record

VenueCompanion to the History of Architecture · 2017
Typeother
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)ArchitectureOriginalityColonialismState (computer science)PoliticsEuropean unionCultural identitySociologyHistoryGenealogyPolitical scienceAestheticsLawAnthropologySocial scienceArchaeologyArtQualitative research

Abstract

fetched live from OpenAlex

Abstract This chapter examines how architectural forms were used to address the question of Canadian identity during the nineteenth century. At every stage, architecture participated in the process of nation building by proposing cultural references, by adapting European and American models, and by elaborating critical responses to existing situations, quite varied from east to west. The chapter is divided into three historical periods, corresponding to the political development of the country: first the “colonial” times and the separation between Upper Canada and Lower Canada; then the “Union era” inaugurated by the Act of Union of 1841; and, finally, the beginnings of the nation‐state “Canada,” grounded in the British North America Act of 1867. In each of these periods, architectural forms can be interpreted as “identity” projects, emphasizing cultural affiliations, regional traits, or hypothetical common denominators. In this manner, the chapter demonstrates that a focus on the question of identity, and on the tensions it implies, promotes a clearer understanding of the specificity and the originality of architectural forms appropriated by architects in Canada. Moreover, it reveals how the identity paradox became a defining problem of nineteenth‐century architecture in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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