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Poverty and the Labor Market: Today and Yesterday

2020· article· en· W3001956771 on OpenAlexaboutno aff
Robert C. Allen

Bibliographic record

VenueAnnual Review of Economics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyExtreme povertyQuarter (Canadian coin)YesterdayDevelopment economicsPopulationEconomicsBasic needsCulture of povertyColonialismGeographyEconomic growthDemographySociology

Abstract

fetched live from OpenAlex

World Bank estimates put absolute poverty in Asia and Africa at 50–60% of the population in 1980 and at negligible levels in the developed world. This review investigates whether Asia was always so poor, as well as the history of poverty in today's rich countries. Poverty measurement methodologies are reviewed, and it is argued that a basic needs approach is the best way to tackle poverty measurement in the past. This approach is related to recent advances in the measurement of historical real wages. Estimates of poverty rates in England between 1290 and 1867 are presented, as are estimates for preindustrial India. About one-quarter of the English population was in extreme poverty in the late Middle Ages, and the proportion had fallen below 10% by 1688. About one-quarter of the people in northern India lived in extreme poverty in the early nineteenth century, and the proportion was likely lower in 1600. The very high poverty rates in India in 1980 were a development of the colonial era.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0050.011
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.023
GPT teacher head0.290
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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

Citations43
Published2020
Admission routes1
Has abstractyes

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