Infrastructure engineer-to-order production systems: Drivers, concepts and principles of quality II and implications for research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Infrastructure Engineer-to-Order (ETO) production systems are often subjected to poor quality and low productivity levels, resulting in time and cost overruns and the dissatisfaction of customers and stakeholders. Quality II emerging from ‘best practices’ in relational ETO supply chains offers a means to improve quality and productivity, but has yet to be recognised as a formal approach that can be explicitly embraced and enacted in practice. In filling this void, we conduct a narrative review to ascertain and discuss the drivers influencing the need for Quality II, examine its underlying concepts, and derive new principles based on people’s well-being, operational performance, and decision-making to underpin its implementation in infrastructure ETO production systems. It is suggested that Quality II will stimulate the learning, innovation, and continuous improvement needed to lift productivity levels in ETO production systems. As Quality II is a nascent concept, we also discuss its implications for research.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it