Bibliographic record
Abstract
The Union of South American Nations (Unasur) presents the most recent vision for trade liberalization and political, economic, and social integration amongst South American countries. Unasur has set 2019 as the year by which it hopes to accomplish many of its goals, such as full regional integration and tariff elimination. But, as 2019 slowly approaches, it remains to be seen whether Unasur will in fact be able to reach these goals. While Unasur’s future is certainly compelling, before heralding Unasur as the long-awaited panacea for pure regional integration, important lessons can be drawn from previous attempts at and iterations of South American regional integration as well as from the model upon which Unasur is structured — the European Union. But what are these lessons, and how do they apply to Unasur? This article will explore the attempts and/or models of Regional Trade Agreements that led up to the creation of Unasur and will then turn to an assessment of Unasur’s successes, failures, the challenges it must face, and an analysis of potential lessons that Unasur may draw from other Regional Trade Agreements.
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 | 0.003 | 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 | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".