Zooming Into Workers’ Psychology and Physiology Through a Lean Construction Lens
Bibliographic record
Abstract
Lean construction has long been a constant advocate for perceiving humans as the driving force for most ventures and projects.Among the enablers of investigating the potentials and capabilities of humans are wearable sensors for collecting physiological measurements.Current research on wearable sensors in construction has not yet touched on its applicability or integration with Lean construction.Therefore, this conceptual paper "zooms into the workers' psychology and physiology through a Lean construction lens" by exploring the potentials of employing wearable sensors in Lean construction.It aims to revamp current applications of wearable sensors by providing a comprehensive overview of the current state of wearable sensor technology and its applications in the construction industry.It also discusses how current studies on wearable sensors may be linked to Lean construction principles and how Lean concepts can further enhance and foster their potentials.The paper concludes by presenting the future possibilities and directions of wearable sensors in Lean construction and the impacts they can have on the industry.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".