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Record W4393184024 · doi:10.29173/mocs24.190

Lessons from Sweden: How Australia Can Learn from Swedish Industrialised Building

2016· article· en· W4393184024 on OpenAlexvenueno aff
Duncan Maxwell, Mathew Aitchison

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyPolitical scienceEnvironmental planningArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Over the past decade, Australia has witnessed increased interest in industrialised building, particularly in the production of housing. This has happened under many different banners, including: prefabricated, modular, transportable and offsite construction methodologies. This interest has grown from a combination of factors, including: increased rate of housing construction and density; rising property and construction costs; the desire for increased efficiency and productivity; and a concern for the quality and sustainability of building systems. Historically, Australia has played an episodic role in the emergence of prefab and transportable buildings since the colonial era, but it does not have a longstanding industrialised building industry. In this context, an analysis of the experiences of North American, European and Japanese examples, provides valuable insights. This paper focuses on Swedenäó»s approach to industrialised building and the lessons it holds for the emerging Australian sector. Sweden represents a valuable case study because of similarities between the two countries, including: the high standard of living, cost of labour, and design and quality expectations; along with geographic and demographic similarities. Conversely, stark differences between the national situation also co-exist, notably climate, business approaches, political outlook, and cultural factors. In the 1950s, Swedish companies exported prefab houses to Australia to combat the Post-War housing shortage, which also supplies a historical dimension to the comparison. Most importantly, Sweden boasts a longstanding industrialised building industry, both in terms of practice and theory. This paper will survey and compare the Swedish industry, and its potential relevance for Australia. Areas of discussion include: the relationship between industry and academy (practice and theory); the diversity of technique and methodologies and how they may be adapted; platform thinking (technical and operational); the staged industrialisation of conventional practices; and the importance of a socially, environmental and design-led practice of building.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.006
Scholarly communication0.0120.007
Open science0.0010.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.046
GPT teacher head0.284
Teacher spread0.238 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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