Feedback from the Source Improving Productivity on Construction Jobsites
Bibliographic record
Abstract
The construction industry relies on skilled trade craftsmen to transfer a concept from design to a physical built entity. In addition, construction jobsites are temporary workspaces. These two characteristics classify construction as a complex production, in which knowledge and experience are gained locally by individuals in the work environment. It also means that workers must adapt and coordinate in the local work environment in order to produce. Without a correct measurement system, the adaption and coordination and resulting information gained are invisible outside of the jobsite, which costs construction contractors and contributes to over $10 × 109 of unnecessary cost to the construction industry annually. ASTM's Standard Practice for Job Productivity Measurement (ASTM E2691-11) was developed to provide a measurement of task, project, and industry productivity. The practice provides ongoing and instantaneous feedback from the source of work on construction jobsites, which are the craftsmen. MCA, Inc. originally developed and implemented this measurement system over fifteen years ago and has seen results of minimum 20 % improvement in productivity on jobsites across the United States and Canada. ASTM E2691 describes how productivity is measured, monitored, and analyzed, and leads to other by-products of information gathered from the source. In addition, ASTM has published Manual 65 on how to use ASTM E2691 with several case applications. This paper explains how ASTM E2691 can be used by all stakeholders of the construction industry supply chain. The standard is explained and examples given for how it can be used by each stakeholder. In addition, a model of information entropy is introduced based on data gathered using JPM, leading to a new approach that can be used to reduce information entropy and improve the time, cost, and quality of construction.
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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 | 0.016 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".