Offsite Evolution: Do we Really Understand the Potential Impact of Technology on Industry 5.0?
Bibliographic record
Abstract
Offsite Manufacturing (OSM) has repeatedly been extoled as a panacea solution for addressing ‘traditional’ construction challenges – ergo: time, quality and cost. However, it is posited here that there are grounds to challenge this preconception, particularly from a technology perspective, given industry’s need to align to Industry 5.0. Arguably (and somewhat contentiously), the industry is still in the process of trying to understand the concepts underpinning Industry 4.0. Thus, in order to understand this perceived disparity, this research used the antecedents from CIB’s Offsite Production and Manufacturing research roadmap (TG74 – publication 372) produced in 2013 as a starting point for discussion. The initial “people-process-technology” constructs were evaluated to establish a starting trajectory. These were subsequently revisited for current/future use - cognisant of the need to align technology-related issues to Industry 5.0. Focus was placed on organisational transition, especially the impact of technology on production, resources and circularity; including the wider societal impact (generational needs, expectations and growth). The research methodological approach adopted used purposive OSM-specific literature, which was synthesised and pattern-matched to confirm the focus and direction of travel. The findings from this were then cross-examined with domain experts using two semi-structured questionnaires and three focus group sessions. The collective findings of primary data was then critiqued, codified and validated against ‘technology maturity readiness’ indicators to highlight organisational priorities and future transition pathways to Industry 5.0.
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 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".