Bibliographic record
Abstract
Prospects for Mexican manufactured exports to the U.S. and Canadian markets should be analyzed in the light of different factors, both domestic and international. Domestically, the economy’s performance is important, particularly after the U.S. recession and our own, the resulting performance of domestic manufactures and the competitiveness problems we face. To start with, we should consider the world economy, and particularly that of the United States, since, as we know, Mexico’s economic cycle has historically been linked to it. Today, expectations for economic recovery in the U.S. do not look very promising for the near future and should be taken into account in any analysis of the prospects for Mexican exports in that market. Other elements must also be taken into ac count, and will be the object of this article: the position of the emerging countries and those in transition that have become our competitors in exports, particularly in our own market. This is the case, mainly, of China and the members of the Mercosur, especially Brazil, as we shall see. We also must not lose sight of what the impact of a Free Trade Area of the Americas (FTAA) would be on our exports to the United States and Canada. Another development that must be kept in mind is the expansion of the Euro pean Union since May 1, 2004 to include ten formerly socialist countries now in transition to a market economy. Is Mexico Losing the U.S. And Canadian Markets?
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 | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".