Bibliographic record
Abstract
This paper analyses the levels and trends of regional disparity and convergence in the two American macro-regions, NAFTA and MERCOSUR. In the case of NAFTA, 95 micro-regions were analysed (12 in Canada; 32 in Mexico; 51 states in the US). In MERCOSUR, the regions are represented by four countries (Argentina, Brazil, Paraguay and Uruguay). The analysis covers the period 2000-2008 (or rather 2000 to 2005 for Mexico).The regional disparities were calculated with the Gini coefficient based on nominal GDP, GDP per capita and GDP per capita PPS. Convergence analysis was done with the Disparity Range Coefficient (DRC), the Average Disparity Range Coefficient (ADRC), σ-convergence and β-convergence.The results of regional disparity were as follows. Based on the nominal GDP, it was at high levels in both macro-regions, with a Gini coefficient above 0.55. With the disparities calculated on GDP per capita, the level of regional disparity in both macro-regions was lower at 0.36 in NAFTA and 0.28 in MERCOSUR in 2000. Based on GDP per capita in PPP, the levels were lower than based on the GDP per capita analysis starting at 0.31 in NAFTA and 0.16 in MERCOSUR. The disparities further decreased by half in NAFTA while slightly increasing in MERSCOSUR.The convergence analysis results based on the DRC analysis showed that neither NAFTA nor MERCOSUR regions converged. The speed of divergence varied significantly. The disparities among the richest and poorest regions in GDP per capita increased 6.26 times more than the average GDP per capita in PPP in NAFTA as a whole. It was only 0.52 in MERCOSUR. The ADRC analysis also resulted in divergence trends for both macro-regions but with lower rates. Convergence calculated with the σ-convergence analysis confirmed that both macro-regions diverged. The divergence rate for NAFTA was 1.41% and for MERCOSUR 0.74. Calculated with the β-convergence analysis, the NAFTA region showed a status quo (convergence of 0.01%) and a divergence trend was registered for MERCOSUR (0.99%). At the country level, the micro-regions in Canada were diverging (1.62% per year) while the ones in the US and Mexico converging (0.02% and 0.77%, respectively).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".