La red de acuerdos y las negociaciones externas del Mercosur
Bibliographic record
Abstract
In its 30 years of existence, Mercosur has strengthened economic relations between its members, increasing interdependence and commercial complementarity among the States Parties and, at the same time, has looked for expanding the negotiations of agreements with extra-regional countries and extra-regional. This trade agenda was promoted from 1994 onwards, beginning with the negotiations for a Free Trade Agreement with the European Union and, until 2011, the bloc has signed 5 trade agreements. Starting from 2017, Mercosur also conducted advanced negotiations with Canada, Singapore, South Korea, Lebanon, and European Free Trade Association. It should be noted that these negotiations are related to the dynamics and transformations of the multilateral trade system. That said, the article analyzes Mercosur external agenda, focusing on the interaction with extra-regional partners. Observing how the discussions on the agreements developed within the Common Market Group (one of the decision-making bodies of the bloc), this study identifies the dynamics in relation to the bloc's external agenda, as well as constant elements and particularities in the cases of agreements in progress, mapping the existing scenario to date.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".