Bibliographic record
Abstract
In this paper we investigate the organizational architecture of development firms. We use the special case of green building which we expect to raise both the uncertainty of direct development costs and payoffs as well as the bargaining costs of development to investigate if and how firms change their firm's structure or contracts to accommodate green building. Previous empirical and theoretical work on the homebuilding industry tends to focus on the size of firms (for example, Somerville 1999; Helsely and Strange 1997). Our focus is more closely related to Eccles (1981) who argues that general contractors and their subcontractors constitute a quasifirms that are not quite fully integrated nor completely market based. We build a model development firm contracting. The model allows us to estimate comparative statics results related to changes in development cost uncertainty and in bargaining costs. We then use evidence from a new survey conducted in the first quarter of 2010 to investigate the predictions of our model. The survey was sent to developers likely to engage in green building, and we obtained 102 useable responses, a 10% response rate. Survey questions focus on firm experience with their first and subsequent green projects. The respondents are mostly privately-held firms well-distributed across the U.S. and engaged in the development of single family, multifamily, office, retail and industrial real estate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".