Automobile Patterns of Diffusion in Four Urban Areas: Comparison of Developed and Developing Countries
Bibliographic record
Abstract
A comparison is presented of the diffusion of the automobile in the metropolitan areas of Paris and Montreal and in two urban areas in developing countries—São Paulo, Brazil, and Puebla, Mexico. The comparison uses a demographic approach, based on the estimation of an age-cohort model. The model takes into account three combined aspects of time: the stage in the life cycle, the generation, and the period. Previous analyses conducted for developed countries indicated that the period effect (including the influence of income) could be considered as residual, in the face of stability of behavior of generations during the observed part of their life cycles. Models for the regions were estimated by using a series of transportation surveys (with the exception of Puebla). The results indicate that comparable patterns can be observed with respect to the life cycle and generation effects, although the ages and generations at which maximum motorization occurs vary among countries, according to their different histories of diffusion of the automobile. On the other hand, the spatial pattern of household motorization presents some fundamental differences for the urban areas of developed and developing countries. Paris and Montreal have higher motorization in the suburbs, but the results for São Paulo and Puebla show the opposite, suggesting the relevance of income effects because the populations in suburban areas have lower income levels.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".