Oportunidades y desafíos para México ante la incorporación al TPP
Bibliographic record
Abstract
El objetivo de este articulo es analizar el papel del Senado de la Republica en el proceso de analisis, discusion y posible ratificacion del Acuerdo de Asociacion Transpacifico (TPP). El texto se divide en tres apartados: en el primero se describen algunos aspectos del Acuerdo a partir de la version final del texto suscrito. En el segundo se esboza el rol del Senado en la ratificacion del Acuerdo, asi como las posturas que en relacion con el mismo que se han presentado al finalizar el primer ano de ejercicio de la LXIII Legislatura (abril 2016). Finalmente, se analizan oportunidades para generar instituciones inclusivas de desarrollo a partir de la incorporacion de Mexico al TPP. El 4 de febrero de 2016 el secretario de Economia, Ildefonso Guajardo Villarreal, suscribio en nombre de Mexico el Acuerdo de Asociacion Transpacifico (TPP, por sus siglas en ingles), en la ciudad de Auckland, Nueva Zelandia. Tambien fue firmado por los ministros de Comercio de los 11 paises restan tes que lo integran: Australia, Brunei-Darussalam, Canada, Chile, Estados Unidos, Japon, Malasia, Nueva Zelandia, Peru, Singapur y Vietnam.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".