The operation, products and promotion of waterpipe businesses in New York City, Abu Dhabi an Dubai
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".