Profit-point analysis: A tool for general contractors to measure and compare costs of management time expended on different subcontractors
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
One of the trends in construction today is the increasing use of specialty contractors. As a result, projects are becoming more complicated and fragmented, more coordination is required, and overhead costs of the general contractors are increasing relative to the direct costs. Better ways of controlling job-site overhead costs are needed. This paper presents profit-point analysis (PPA), a method for analyzing how indirect staff time of a general contractor is actually spent on a project. Profit points are imaginary points where a general contractor and subcontractors are interfaced. The PPA is a method of analysis on these points, which adapts activity-based costing from manufacturing to construction. This new method, illustrated through a case study, yields valuable information for managerial control; for example, the different amount of supplemental support from the general contractor required by different subcontractors.Key words: overhead costs, cost analysis, profit points, activity-based costing (ABC), management efficiency, evaluating specialty contractors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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