Bibliographic record
Abstract
Pese a los numerosos tratados de libre comercio que Mexico ha fi rmado, no en todos los casos han ayudado a incentivar el intercambio comercial con esas naciones, segun estadisticas presentadas por la Secretaria de Economia. Esto se observa en las exportaciones no petroleras, al comparar la situacion que habia en el ano previo a la fi rma del Tratado de Libre Comercio de America del Norte (tlcan) respecto a la actual, contemplando el periodo enero-agosto de 2010. Por ejemplo, en 1993, un ano antes de que entrara en vigor el Tratado de Libre Comercio de America del Norte (tlcan), el comercio total de Mexico con eeuu y Canada represento 78.2% del comercio total que realizo el pais, y en enero-agosto de 2010 se redujo a 65.8%. Se cree que Mexico no podra subirse al tren chino, que sera el que arrastre a la economia mundial los proximos anos. De acuerdo con los datos que acaba de publicar el fmi, al termino de este ano la economia de Estados Unidos tendra un valor de 14.6 billones (trillions) de dolares ajustados a paridad de poder de compra. La economia china, por su parte, terminara este ano como la segunda del mundo con un valor de 10.1 billones de dolares, a partir de la misma medicion. Si, por su parte, consideramos que la economia china (pesimistamente) “solo” creciera a una tasa de 6% en promedio en ese mismo lapso, terminaria la decada con un pib de 18.1 billones, apenas inferior al de Estados Unidos. Mural (26/11/2010). “Los tlc de Mexico.” En: http://busquedas.gruporeforma.com/mural/Documentos/DocumentoImpresa.aspx
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".