Development of proposals for improving the pricing of construction products
Bibliographic record
Abstract
The purpose of the article is to explore the current situation in the pricing reform as of the 2nd quarter of 2023 using the example of costing for operating machines and mechanisms in calculating the estimated cost on the basis of the resource-and-index method. The methodological basis of the study is represented by economic and statistical analysis, systemic approach, systematisation of theoretical and practical material. The analysis of estimated prices for operation of machines and mechanisms is made on the basis of the ones posted in the Federal State Information System of Construction Pricing as of the 2nd quarter of 2023 and calculated in current prices with the use of estimated cost change indices relative to a group of homogeneous resources. Most of the reviewed examples for basic machinery and mechanisms used in construction show a negative deviation when calculated using the estimated cost change index. The use of prices for the operation of machinery and mechanisms, obtained using the estimated cost change index, can lead to underestimation of costs required for construction and, as a consequence, emergence of obstacles to successful realisation of a construction project. Based on the results of the analysis, proposals have been made to improve the construction products pricing on the basis of due costing for operation of machinery and mechanisms. It is necessary to envisage a number of measures aimed at successful realisation of investment and construction projects and due development of the construction sector as a whole.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".