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Record W257973895

The 60s: Montreal Thinks Big

2005· book· en· W257973895 on OpenAlexaboutno aff
Olivo Barbieri, Marcel Fournier, André Lortie, Michael Sorkin, Jean‐Louis Cohen

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionVisionArchitectureArt historyMedia studiesHistoryVisual artsArtSociologyAnthropology
DOInot available

Abstract

fetched live from OpenAlex

the 1960s, Montreal, like many other large cities - Paris, Philadelphia, and Rio among them - embarked on a program of change on a monumental scale. With its metro, underground shopping promenades, and 1967 world's fair, it stood out from the rest in a remarkable way. In Montreal, new skyscrapers and expressways fundamentally transformed the architectural and urban landscape without permanently compromising the viability of the city centre. Archetypal among North American and European cities affected by the same phenomenon, Montreal remains unique because of the vision that shaped its development. This richly illustrated volume explores the ideas that were to define Montreal's future - ideas whose effects are still being felt today. Framed by a striking photographic essay, it includes contributions by a group of diverse and distinguished scholars, together with a wealth of drawings, maps, charts, models, photographs, and literary vignettes that reveal the perceptions and visions of urban planners, architects, writers, and artists of the period. Published to accompany the exhibition The 60s : Montreal Thinks Big, organized by the Canadian Centre for Architecture in Montreal, this book offers a new look at the social and architectural legacy of a momentous decade -- Front flap of cover.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.100
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.006
Scholarly communication0.0100.005
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0490.006

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 head0.195
Teacher spread0.182 · 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
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

Citations5
Published2005
Admission routes1
Has abstractyes

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