Making Inferences About Rich Country - Poor Country Convergence: The Polarization Trapezoid and Overlap measures
Bibliographic record
Abstract
Underlying the unresolved debate over whether the gap between rich and poor country GNP per capita has narrowed is a concern for wellbeing. The issue is really about the changing shapes of distributions of wellbeing indicators. As limiting cases con- vergence between rich and poor country groups can be brought about by countries within groups becoming less alike without any diminution of growth rate differentials between them or it can be brought about by reductions in these differentials without any diminution of within group identity. In essence the debate is about the extent to which rich and poor countries are polarizing, a subject first theoretically explored by Esteban and Ray (1994). The empirical issue is about whether separate groups can be identified in the overall distribution and whether they are tending toward common or distinct equilibria. This paper proposes two simple statistics for the problem, the Overlap measure and the Trapezoidal measure, changes in which reflect a combination of increasing (decreasing) subgroup location differences and decreasing (increasing) subgroup spreads which are the characteristics of polarization (convergence). The former statistic is of use when the sub-distributions are identified, while the latter can be used whether or not the subgroups are identified. These techniques are applied to the examination of convergence in GDP per capita between rich and poor nations when growth is viewed either as a wellbeing index or a technology index (i.e. the data are, or are not, population weighted). It turns out that such a distinction matters, viewed technologically there is divergence, viewed in a wellbeing sense there is convergence. As a collection of countries Africa is diverging from the rest of the world whatever the perspective of growth.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".