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Record W1462078538

Al borde de la incertidumbre : reduccion de la pobreza en America latina y el caribe durante y despues de la gran recession

2011· article· es· W1462078538 on OpenAlexaboutno aff
Louise J. Cord, Carolina Diaz Bonilla, João Pedro Azevedo

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansRecessionPovertyEconomicsPer capitaDevelopment economicsGross domestic productQuarter (Canadian coin)Extreme povertyPoverty reductionPurchasing power parityGeographyPolitical scienceDemographyEconomic growthPopulationSociology
DOInot available

Abstract

fetched live from OpenAlex

Strong poverty reduction in Latin America resumed with the growth rebound in 2010, as both moderate and extreme poor households benefitted from the recovery, accelerating poverty reduction to rates similar to those witnessed between 2003-2006 despite a 2.8 percent decline in Gross Domestic Product (GDP) per capita in Purchasing Power Parity (PPP) terms, poverty levels in Latin America (LAC) remained basically static during the great recession, as the poor were shielded from the economic crisis in some countries and continued to benefit from growth in others. In 2010, poverty reduction resumed sharply in Latin America, as household incomes were once again closely linked to economic growth at rates similar to pre-crisis years. Moderate poverty declined by almost 2.5 percentage points to reach 28 percent in 2010, while extreme poverty fell by more than 2 percentage points to reach 14 percent. As 2011 comes to a close, once again the global economy and Latin America are facing risks of yet another economic slowdown. Using household survey data from 2010 and selected labor market indicators through the third quarter of 2011, this note identifies some basic facts on the impact of the crisis and the recovery on the poor and explores their implications for poverty reduction in the region going forward.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.353
Teacher spread0.332 · 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 designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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