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Record W4389198002 · doi:10.15581/014.25.118-137

Brutalism, Film, and Dystopia: The Many (Cinematic) Lives of John Andrews’s Scarborough College

2023· article· en· W4389198002 on OpenAlexaboutno aff
Luis Miguel Lus Arana, Stephen Parnell, Gerardo Martínez-Marco

Bibliographic record

VenueRa Revista de Arquitectura · 2023
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsDystopiaMovie theaterArt historyArtPersonaGovernment (linguistics)SociologyLiteraturePhilosophyHumanities

Abstract

fetched live from OpenAlex

Since its opening to the public in 1965, John Andrews’s megastructure Scarborough College —currently University of Toronto, Scarborough— has received universal acclaim. Praised by Kenneth Frampton as “by far the most daring, comprehensive and radical”¦ of all the completed university complexes of recent years”, Scarborough has enjoyed, unlike many other brutalist structures, a peaceful and successful existence: untainted by later extensions, the Andrews Building, as it is known today, has not only survived, but become the revered core of a landmark university campus. However, adding to its prosperous history as a university facility, Scarborough has also led a prolific double life as a filming location, lending its architectural persona to an assortment of evil corporations, futuristic prisons, or government facilities. This article examines some of the many appearances of Scarborough on the screen, and the different ways in which cinema has depicted, appropriated, recontextualized, transformed, and even extended the building beyond Andrews’s original design and its as-built reality.

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)0.003
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 score0.942
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.014
Scholarly communication0.0100.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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