French Quarter Economic Development District for New Orleans, LA, 2018
Bibliographic record
Abstract
This polygon shapefile represents French Quarter Economic Development District for New Orleans, LA in 2018. Voters have elected to fund enhanced security in the French Quarter through a quarter cent sales tax increase within the boundaries of the French Quarter, an area where about 9 million tourists spend money each year. Effective January 1, 2016, all businesses will be required to collect an additional 0.2495% in sales/use tax on taxable items and services sold or delivered into the new French Quarter Economic Development District (French Quarter EDD). Funds generated from an additional quarter cent sales tax within the boundaries of the French Quarter Management District (see map below), will be used to form the French Quarter Economic Development District and fund full-time Louisiana State Police trooper patrols in this area. This added security for the residents, workers and visitors of the French Quarter would supplement the New Orleans Police Department services already committed to the area. An additional quarter cent sales tax in the French Quarter Management District will generate at least $2 million each year for public safety. These funds would be generated primarily by the tourists who visit the French Quarter. If passed, hospitality organizations would match these funds for state troopers. Plus, the City of New Orleans would allocate $500,000 from its portion of the hotel self-assessment. All told, if passed, at least $4.5 million will be generated to support a minimum of 30 full-time state troopers. This file was downloaded on December, 2018 by NYU Data Services. It was originally represented on the New Orleans Open Data Socrata portal as catalog record xcvk-8kcc
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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; both teacher heads agree on what is shown here.
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".