Bibliographic record
Abstract
This paper provides an overview of poverty in North America. In it we look at the three countries of North America, Mexico, the US, and to a lesser extent Canada and attempt to both describe poverty as it exists in the three countries and explore some of the correlates of poverty. In doing so, we attempt to bring together the concepts and approaches used mostly in studying poverty in developing countries and those used in developed countries. We propose some definitions of poverty that we believe can be usefully applied across very different countries. We explore some correlates of poverty in the three countries, and both the similarities and the differences in the correlates of poverty across the three. We take note of the policy issues that are raised by these relationships. As might be expected, we raise more questions that we answer, about both our approach and our findings. We begin with an overview of growth and inequality in the three countries. The second section of the paper presents concepts and measures of poverty and reports the overall incidence of poverty in the three countries using various measures. The third section explores the relationship between the level of economic development and poverty, both between and within countries. The fourth section looks at the relationships between household composition and poverty and between race/ethnicity and poverty in the US and Mexico. The final section briefly raises policy issues that emerge from the analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".