Bibliographic record
Abstract
Global capitalism, vintage early 21st century, favors the movement of goods and capital across national borders more than it does the movement of people. It was not always this way. The first wave of globalization, in the second half of the 19th century and the early 20th, came with massive international migration. Around 60 million people migrated from Europe to the countries of the New World (Argentina, Australia, Brazil, Canada, and the United States) over a period of 40 years or so. In a sense, current globalization has a smaller degree of"cosmopolitan liberalism"in thedimension of international migration. While there is consensus on the benefits of an open trade regime and relatively liberal capital movements, that consensus rarely extends to the free movement of people. Solimano examines this difference in the"freedom to become global"by looking at both standard trade theory, basically the Mundell theorem of trade and migration as substitutes, and the ensuing analytical developments and empirical evidence around the Mundell result. He then looks at this asymmetry in today's global economic order from the perspective of freedom, individual rights, and transnational citizenship, as well as the potential of international migration to reduce global inequality. Preventing factor (labor or human capital) movements from lower- to higher-productivity activities (countries) may entail a global welfare loss in terms of forgone world output (although the distributive consequences for sending and receiving countries vary). International migration tends to reduce income disparities across countries. But it can increase inequality within labor-scarce receiving countries by moderating the growth of wages, because of the associated increase in the supply of labor. In contrast, in sending countries emigration can have an equalizing effect by reducing the supply of labor and raising wages. Still, international migration is bound to have a positive effect on long-run growth in receiving countries by keeping labor costs down, increasing the profitability of investment, and raising national savings. For sending countries, the impact on growth depends on the pool of labor and human resources that emigrate. In labor-abundant developing countries with chronic unemployment (or labor surplus), the growth-depressing effects of emigration can be small (compensated in part by labor remittances). Nevertheless, the emigration of highly educated people, professionals, and national investors can have a detrimental effect on long-run income levels and growth rates for sending countries. Froma global perspective, however, world output would be expected to increase if people could freely move across the planet from areas of low labor productivity to areas of high labor productivity. From the viewpoint of global economic freedoms, the result would be equally positive.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".