Bibliographic record
Abstract
This report reviews methods adopted in the United States and Canada for determining fairness and efficiency in rail markets and discusses their potential application in Mexico. It specifically examines how waybill and financial data are used in the economic regulation of railways and makes recommendations for establishing a data collection and analysis system suited to the Mexican railway system. Mexico has transformed its loss-making national railway into profitable concessions that have invested in infrastructure and carry growing volumes of freight. Some of the provisions agreed in the concession titles regarding interconnection and competition on specific links have not, however, developed as expected. A new regulatory agency was established in 2016/17 to address this and establish the capacity for the government to intervene effectively where necessary. A top priority for the Agencia Reguladora del Transporte Ferroviario de México is to develop a data collection and analysis system to understand rail markets in relation to issues of potential abusive pricing and reasonable conditions of access.
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 | 0.066 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".