The Borders of Inequality: Where Wealth and Poverty Collide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it