Bibliographic record
Abstract
Only 43 per cent of U.S. construction firms remain in business after four years. Why? Inadequate management, according to the U.S. Small Business Administration. This is surprising because most construction firms are formed by ambitious construction project managers, executives and tradesmen who have excelled at what they have been doing. But as experienced as these entrepreneurs may be, they are not likely prepared to take on the full range of responsibilities forced on them in managing the business of construction in its entirety.While this business failure rate and its causes are based on U.S. experience, available data from a number of other industrialized countries shows they are similar.This book describes in detail what the business side of the construction equation requires of the construction firm owner. The contractor who quickly learns these requirements can identify and avoid or manage around the pitfalls that cause the high failure rate in our industry and put his or her construction firm on a level playing field with the best-run companies in the business. The detailed duties of the owner, whether in the U.S., U.K., Australia or Canada, are a common theme throughout the book. The author, Nick Ganaway, speaks peer-to-peer, and the book is sprinkled with supporting examples from his own experience. He is immersed in the industry and this book is "based on the things I've learned, used, and refined as a light-commercial general contractor in the course of starting and operating my own construction firm for 25 years." The contractor doing $5 million or $50 million or more in annual sales or the equivalent amount in other countries, or the entrepreneur who is just starting up, can use the tried and proven material in this book to build a business that is profitable, enjoyable, and enduring.Additionally, the book devotes a chapter to specializing in chain-store 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.134 |
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".