Efeitos da recessão econômica sobre a situação de pobreza no Brasil
Bibliographic record
Abstract
This research aims to analyze the effects of the Brazilian economic recession from the second quarter of 2014 to the fourth quarter of 2016 on the situation of poverty in Brazil and to determine which individual characteristics are most associated with the state of vulnerability. It composes a poverty profile based on the micro database of the Continuous National Household Sample Survey (Continuous PNAD), available between 2012 and 2019. For this work, the poverty line was that recommended by The World Bank, which defines poverty as families with per capita income below US$ 5.50 per day in terms of Purchasing Parity Power (PPP). The estimation of poverty indicators is carried out by the indicators proposed by Foster, Greer and Thorbecke (1984). The logistic regression model was utilized using the maximum likelihood technique. The results showed that poverty rates were higher for residents in the Northeast and North regions, as well as among residents in rural areas. Regarding the demographic profile, there were higher rates of poverty in younger age groups and among people who declared themselves black or mixed race. There were no major differences between men and women. Regarding the level of schooling, the highest poverty rate was observed among people with a lower level of formal education.
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How this classification was reachedexpand
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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 teacher head, 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".