Quick commerce: will the disruption of the food retail industry happen? Investigating the quick commerce supply chain and the impacts of dark stores
Bibliographic record
Abstract
The transition to food e-commerce is made possible by the implementation of new technologies, by the arrival of new players who have made the sector more complex and dynamic, and by the development of new logistical capacities (dark stores, micro-hubs). The quick commerce sector marks an additional step in the development of instant deliveries, which now applies to the entire food and online shopping market. This research analyzes the organization of the quick commerce supply chain (organization based on logistics micro-hubs (dark stores), partnerships with large-scale distribution, third-party logistics operators, organization of the last mile) and also highlights the highly transport-intensive nature (in terms of vehicle movements and delivery flows) of dark stores. The establishment of dark stores in densely populated areas (Paris, London, New York, etc.) is accompanied by controversy with local elected officials about the nuisances generated by the activities of dark stores (noise, congestion, pollution, waste, non compliance with local urban planning rules, aesthetics linked to blacked-out facades) and about the negative externalities, real or supposed, of quick commerce on local commerce and urban life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".