Factors Influencing Construction Clients’/Contractors’ Choice of Subcontractors in Nigeria
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
Arising from the recent shift in the attitude of main contractors to subcontract procurement in some of the developing countries of the world, this study presents the findings of the importance of factors influencing the choice of subcontractors by the clients and contractors. With a focus on three commercial nerve centres of Nigeria (Lagos, Abuja and Port Harcourt), the study presents the findings of a survey of construction clients/contractors and rank the factors they consider in the selection of suitable subcontractors for project execution. The results of the relative index ranking technique indicate that five most important factors are: subcontractors’ past experience in terms of size and type of projects completed; subcontractors’ management resource in terms of formal and informal training; other related issues in terms of nature of contract and time of the year (weather), past relationships with the clients/contractors (past performance), and project facilitation in terms on labour/plant resources. It is concluded that greatest premiums should be attached to some of these factors for improved construction sustainability.
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 | 0.003 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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