Bibliographic record
Abstract
Abstract Mexico City’s subway, commonly known as “el Metro,” opened its first line of service on September 4, 1969. Since then, the mass transit system, operated by the Sistema de Transporte Colectivo (STC), has expanded to include 195 stations across twelve lines that serve an estimated five and a half million riders per day. The metro was constructed not only to alleviate severe traffic congestion in the city’s center due to population growth and private car use, but also it was envisioned as part of a plan to modernize the city and raise Mexico to the status of world cities such as Paris and Montreal. The low fare has made it one of the primary modes of transportation for the city’s working class, who use it in combination with other forms of public transportation to reach jobs in distant parts of the metropolis. Some studies have shown that the Metro has exacerbated geographic segregation between rich and poor as well as perpetuated low wages. Beyond its function as a mass transit system, the Metro was envisioned as and still serves as an important cultural space. The graphic designers and architects who led the project integrated modern architectural elements with graphic embellishments and signage that incorporated national culture and history to present a modernity uniquely Mexican. In its almost fifty years of service, the Metro has become an important symbol of the capital’s cultural life that everyday Mexicans have used for their own political, economic, and cultural purposes.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".