Upstairs in the Vieux Carré: An Analysis of Incentives and Tools to Catalyze Private Sector
Bibliographic record
Abstract
Millions of tourists flock to New Orleans’s famed French Quarter each year to enjoy its offering of unique culture and historic buildings. However, many of the low-rise historic buildings are in need of rehabilitation. These blighted structures threaten the vitality of the Vieux Carré Historic District. This thesis examines public sector incentives and tools to determine their ability to catalyze private sector rehabilitation of the French Quarter’s low-rise historic buildings. A list of extant incentives and tools were compiled and tested using two hypothetical projects, each representing a different type of common French Quarter building typology. The goals of this financial analysis were to determine which incentives provided the greatest monetary impact and whether or not the impact was sufficient to motivate the private sector to undertake the project. The analysis determined that state and federal historic tax credits were the most effective incentives, but that they were not adequate to spur a significant amount of rehabilitation activity on the French Quarter’s low-rise buildings. A sensitivity analysis was conducted on both federal and state historic tax credits to determine the appropriate tax credit rate. The analysis determined that combined tax credit rate of up to 90 % of Qualified Rehabilitation Expenditures was necessary to achieve the necessary unleveraged yield for real estate developers to undertake these small-scale rehabilitation projects. Other recommendations included the adoption of tiered tax credit
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".