The evolving retail structure of mexico city
Bibliographic record
Abstract
A recent economic census of Mexico City reveals the complexity of the retail structure, in terms of stores, sales, and location of its various components, ranging from public markets to traditional retail streets to more modern innovations like shopping malls and power centers. Data from an origin–destination survey for transportation identifies the characteristics of customers for various destinations, and permit us to position the city within a general sequence of retail evolution, roughly linked to income, hence level of economic development. Traditional locations such as downtown, the public markets, and retail shopping streets, have a long history in the city and largely serve the low-income population. More modern retail developments such as shopping centers, supercenters, and power retail have recently emerged to serve the well-to-do. This bifurcation of retail facilities and their clients is exacerbated by the extreme income inequality in the city, and by the fact that the automobile has become a fundamental indicator of social class in Mexico City. Without a car, the household depends on the elements of traditional retail—public markets and nearby retail streets—whereas households with a car are able to shop in the facilities of modern retail: supercenters, shopping centers, and big box stores.
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 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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".