The Legacies of Manufacturing and Factories of Industrialised Construction
Bibliographic record
Abstract
The term ‘industrialised construction’ carries the promise of an industry transformed, an industry driven by improved processes and higher quality products. One of the more obvious differences between industrialised construction and traditional construction is the factory. Yet it is often undervalued as a secondary consideration to the seemingly more important factors of speed, efficiency and economic rationalisation. This paper offers a reconsideration of the history of the factory as a critical feature in shaping contemporary sites of production in the construction industry. While the manufacturing mega-factories of today continue to develop at a rapid rate, their composition has been shaped by all three previous industrial revolutions and the current fourth. Drawing on the legacies of mechanisation, mass production and automation, today’s factory is informed by ideas of lean and agile production, and the connected factory forecast by Industry 4.0 looks towards the internet, cloud and IoT in visions of the future. By charting the evolution of the preceding three phases of industry in relation to key architectural developments of the factory, this paper reflects upon which aspects of these earlier chapters of manufacturing have affected the implementation of Industry 4.0 in the industrialised construction sector. Research in this area has often asked what the production sites of industrialised construction can learn from contemporary manufacturing, such as the automotive, aerospace or technology industries. By contrast, this paper questions the how the potential requirements of industrialised construction might differ from other forms of manufacturing and how this might in turn inform future sites of production in this sector. This paper speculates that a contemporary industrialised construction industry would be wise to re-evaluate the factory as a space specific to construction, distinct from manufacturing origins, in order to better address the broad range of new, or previously under-considered, industry specific requirements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".