Bibliographic record
Abstract
The food delivery market has started to migrate to platforms rapidly. One reason food delivery platforms benefit this market is the potential to optimize courier routing by sharing couriers among many restaurants. We address the following questions: First, how courier sharing contributes to the reduction of delivery costs? Second, how platforms affect restaurant positioning and food delivery market coverage. To address these questions, we consider a spatial queuing model in which couriers are the servers. We model traditional restaurant delivery with dedicated couriers as two M/G/1 queues in which each courier works for one restaurant, and the platform delivery as two M/G/2 queue in which couriers are shared between restaurants and can take orders from any of them. We show that sharing couriers attains a shorter wait time for a market with a sufficiently high arrival rate and a sufficiently high sensitivity to taste among customers. This will result in higher customers' welfare and consequently drives up prices. Moreover, when the market is sensitive enough to taste, sharing couriers can lead to an increase in restaurant variety and market coverage. We then make extensions from a linear city to a two-dimensional space with the Manhattan distance and to account for asymmetric restaurants.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".