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Record W7067516410

Metro Centre: The “Very Obvious Job” that was never Built

2023· book-chapter· en· W7067516410 on OpenAlexaboutno aff

Bibliographic record

VenueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownPlan (archaeology)Context (archaeology)PedestrianUrban planningUrban design
DOInot available

Abstract

fetched live from OpenAlex

One of the largest and most ambitious projects in John Andrews’s career, Metro Centre was an imposing yet unaccomplished development plan for Toronto’s downtown that was elaborated between 1968 and 1971. Targeting a large area of underused railway tracks dividing the city from Lake Ontario, the scheme was one of the first opportunities for Andrews to operate at urban scale in an already established context, confronting with existing street patterns, traffic flows, and pedestrian movements. The essay looks at the history of the project, in particular at the involvement of the clients – Canadian National Railways and Canadian Pacific Railway —, at the role played by the design team, at the specific context of Toronto and Canada between the end of the 1960s and the early 1970s, and at the fate of the entire operation. The plan for Metro Centre reveals key aspects of the design philosophy that Andrews espoused, but it is also emblematic of the historical period and the cultural climate of the time.

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)0.000
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 score0.142
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.004

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 head0.098
GPT teacher head0.222
Teacher spread0.123 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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