The Borders of Inequality: Where Wealth and Poverty Collide
Bibliographic record
Abstract
Recently U.S. media, policymakers, and commentators of all stripes have been preoccupied with the nation s border with Mexico. Airwaves, websites, and blogs are filled with concerns over border issues: illegal immigrants, drug wars, narcotics trafficking, and securing the border. While this is a valid conversation, it s rarely contrasted with the other U.S. border, with Canada still the longest unguarded border on Earth. In this fascinating book, originally published in Spain to much acclaim, researcher Inigo More looks at the bigger picture. With a professionally trained eye, he examines the world s top twenty most unequal borders. What he finds is that many of these border situations share similar characteristics. There is always illegal immigration from the poor country to the wealthy one. There is always trafficking in illegal substances. And the unequal neighbors usually regard each other with suspicion or even open hostility. After surveying the top twenty, More explores in depth the cases of three borders: between Germany and Poland, Spain and Morocco, and the United States and Mexico. core problem, he concludes, is not drugs or immigration or self-protection. Rather, the problem is inequality itself. Unequal borders result, he writes, from a skewed interaction among markets, people, and states. Using these findings, More builds a useful new framework for analyzing border dynamics from a quantitative view based on economic inequality. The Borders of Inequality illustrates how longstanding multidirectional misunderstandings can exacerbate cross-border problems and consequent public opinion. Perpetuating these misunderstandings can inflame and complicate the situation, but purposeful efforts to reduce inequality can produce promising results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".