Annual Meeting of the American Political Science Association in Toronto, CA. I am grateful to
Bibliographic record
Abstract
Kurtz. All errors are my own. In the final decades of the twentieth century, developing nations around the world defied expectations by undertaking – and sustaining – deep movements toward economic and political liberalization. Defying concerns that fledgling democracies would be destabilized by the dislocations attendant upon such transitions, democratic regimes have perdured amidst an array of social and economic upheavals. In other ways, however, the performance of young democracies in the context of economic integration has often fallen short of what scholars and citizens anticipated. Even though the widely-employed Heckscher-Ohlin-Samuelson (HOS) model predicted that openness would bring relative gains in income to the abundant low-skilled laborers in the developing world, income inequality instead has worsened in most cases (Goldberg and Pavcnik 2007; Wood 1999). And young democracies often have failed to satisfy expectations that the poor in societies riven by sharp income disparities could impose meaningful redistribution on the better-off (Meltzer and Richard 1981; Acemoglu and Robinson 2006). Economic development and democracy in many, but far from all, developing countries thus have been achieved despite high levels of economic inequality. In many cases, democracy has made little noticeable impact on those disparities, leaving large numbers of citizens trapped in grinding
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.389 | 0.212 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".