A National Multilevel Analytical Research Agenda to support Integrated Construction Supply Chains for Offsite Housing Systems
Bibliographic record
Abstract
It is often speculated that offsite manufacturing can be an important part of creating a built environment sector that is ‘smarter’, safer, more efficient and innovative and environmentally ‘friendly’. The construction industry often faces various challenges and offsite manufacturing is not the only panacea for the industry ills. However offsite manufacturing can achieve various economic, social and environmental aspirations. The sector is composed of numerous diverse players and this is one of the greatest challenges when initiating transformative changes. This inertia exists with offsite manufacturing. The paper shall discuss the current emergence of a research agenda in Australia. The potential for offsite manufacturing to be an anchor to bring industry and academia together has merit and the 2014 Strategic Roadmap for Integrated Construction Supply Chains for Offsite Housing systems Research Capability in underpinned by this focus and articulates the priority areas for national and collaborative research over the next five years. The Roadmap was developed with the support of the Australian China Science Research Foundation. The Priority Areas are an important element in strengthening the integration potential and collaborative capacity in the Australian built environment supply chain industrial systems. This Roadmap is concerned with national research projects at a small to medium scale, likely to have a strategic impact on research and industry practice in Australia. It has been developed through an analysis of research that is conducted in other countries that have a maturity in this area, followed by a comparison with Australian research that is missing in our programs currently. This was then aligned with a reflection on the barriers and enablers in current and emerging practice. It is also informed through interviews with leaders in our sector who are grappling with entering the OSM market or who have been engaged in the market for some time. The Department of Industry funded a mission which included a visit to China to analyse a more mature off site manufacturing industry. Nine site visits and interviews with three research institutions and six industry organisations provided date for an international comparative analysis and the Roadmap.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".