Testing Stochastic Convergence among Mexican States: A Polynomial Regression Analysis
Bibliographic record
Abstract
Another look on the economic convergence among Mexican states is offered examining whether they are approaching along 1940-2010. Methodology is based on polynomial regressions, a method that determines whether predictions can be significantly improved by increasing the complexity of the fitted straight-line model. Estimates from a set of polynomial terms are a theoretical approximation to income differentials, so it constitutes an adequate frame to analyze if different initial conditions tend to diminish in the long-run. We calibrate for each economy the polynomial equation of best adjustment supported in information criteria and a strategy of backward iterative elimination. Empirical results are according with the stochastic convergence, but in a relationship where it changed after trade opening, poorer states are diverging and richer states are converging. A focalized regional policy is necessary with the aim to correct the biases produced in a context where some regions are lagging while others more are advancing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".