In-House Delivery of Multiple-Small Reconstruction Projects
Bibliographic record
Abstract
As compared with new construction, reconstruction of operational facilities exhibits a higher challenge, particularly when multiple projects are involved. For owner organizations involved in such projects, use of in-house resources versus outside contractors has been a major dilemma, with each approach having its potential benefits. This paper uses a real-life case study approach to investigate the delivery of 800 small reconstruction projects using in-house forces. Details are described related to the prioritization, budgeting, organization structure, and the mechanisms used for staff allocation. It was found that the main characteristics of projects that are best delivered by in-house forces include high urgency and inadequate scope definition. Outsourcing this type of projects exposes the owner to a large number of changes and their consequent cost overruns/delays. Based on the case study, the challenges facing in-house delivered projects and the factors that contribute to their success were investigated and outlined. To verify the findings a questionnaire survey among similar organizations is conducted and its results discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".