Brazil Poverty and Equity Assessment : Looking Ahead of Two Crises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2020, Brazil was about to face socioeconomic disruptions of historical proportions. The onset of the COVID-19 pandemic broke several undesirable Brazilian records. First, the pandemic wreaked an enormous direct human toll, sickening millions and causing the death of 195,441 Brazilians in 2020 and 619,056 in 2021. Second, the Brazilian economy experienced its worst contraction in recorded history, with real gross domestic product (GDP) per capita growth in 2020 at -4.7 percent (compared to the previous record of -4.4 percent in 2015). Third, COVID-related closures and other measures led to a massive, unprecedented exit of workers, with an estimated 10 million people leaving the labor force between the third quarter of 2019 and the third quarter of 2020. The economic crisis induced by the pandemic is the second in Brazil’s recent economic history, following the 2014 to 2016 crisis. These downturns have nearly halted its poverty reduction progress and widened disparities in what was already one of the most unequal countries in the world. The Brazil Poverty and Equity Assessment takes an analytical approach to study the situation of the Brazilian population as they were facing these economic shocks. With a focus on the more recent pandemic shock, the report combines household survey, administrative and phone survey data to: i) analyze how the most vulnerable weathered the impacts of the pandemic and how the support of the government provided protection during this time; ii) present an in-depth profile of the monetary poor and vulnerable, including data from traditional communities not published before; iii) understand the non-monetary vulnerabilities of the population such as the risks to climate change events; and iv) discuss public policy implications that can help tackle the deep rooted causes of poverty.
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.
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it