Governing Collaboration: Data and Work Relationships in U.K. Software for Building Design, 1970–1980
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".