The operation, products and promotion of waterpipe businesses in New York City, Abu Dhabi an Dubai
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We evaluated the customers, operations, products and advertising of these businesses to explore the unique policy challenges created by the suppliers of waterpipes. We completed a cross-sectional survey consisting of structured site observations and in-person interviews of businesses in New York City, Abu Dhabi and Dubai identified using Google, Yelp, Timeout Dubai and Timeout Abu Dhabi and neighbourhood visits in 2014. Regular customers made up 59% of customers. Franchises or chains were 28% of businesses. Waterpipes made up 39% of sales with 87% of businesses offering food within their menu. Flavoured tobacco made up 94% of sales. Discounts were offered by 47% of businesses and 94% of businesses used advertising, often through social media. The market consists of largely independent businesses, with a large regular customer base, frequently offering diversified services beyond waterpipes. These businesses advertise using both traditional and social media. The economics of waterpipe businesses is very different from the economics of cigarettes, and unique regulatory strategies are needed to control this epidemic.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it