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Record W3122123856

Poverty and Place in North America

2005· preprint· en· W3122123856 on OpenAlexaboutno aff
Mary Jo Bane, René Zenteno

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPovertySection (typography)Development economicsEthnic groupInequalityRace (biology)Basic needsGeographyEconomic growthPolitical scienceEconomicsSociologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.351
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicIncome, Poverty, and InequalityFrench-language works237,207