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Record W4411171652 · doi:10.1109/mahc.2025.3577585

Governing Collaboration: Data and Work Relationships in U.K. Software for Building Design, 1970–1980

2025· article· en· W4411171652 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Annals of the History of Computing · 2025
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsWork (physics)Software engineeringSoftwareComputer scienceEngineering managementEngineeringSystems engineeringProgramming languageMechanical engineering

Abstract

fetched live from OpenAlex

In the 1970s, the UK government saw coordination through digital models as the remedy for how various participants involved in complex architectural projects could effectively work together. Government agencies responsible for public buildings, such as hospitals and housing, hired architects and technologists to develop software for building design — computer systems that described existing building methods as digital models and construction databases. The article examines two such systems to detail how their novel databases encoded the building and administrative approaches of their agencies. In doing so, it argues that, while the focus was on automating clerical design work such as material calculations and detailing, the software ultimately implemented overarching frameworks of design regulation, restructuring the design team. By linking data structures to structures of control, the article contributes critical insights into how architectural software for collaboration makes design work discrete and governable.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

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

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 head0.092
GPT teacher head0.291
Teacher spread0.198 · 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