Determinants of Long-Term Unemployment in Brazil in 2013
Bibliographic record
Abstract
This work analyzes the determinants of the probability of a Brazilian worker being unemployed for more than a year, using data from the 2013 National Household Survey (PNAD) and applying a probit model. The results show a lower chance of remaining jobless of males, heads of households, those who declared themselves black, younger people, those who completed higher education or are in the process of acquiring it, and residents of the Southeast and South regions of Brazil. The probabilistic scenarios show that the Brazilian workers least likely to remain unemployed for over a year are males, residents in the South or Southeast region, heads of a household, between 36 and 45 years of age, with higher education, with only a 0.6% chance of remaining in that condition. On the other hand, the workers with the highest chance of remaining unemployed are females, between 46 and 65 years old, residents in the North region, illiterate and not household heads, with a 41% probability of remaining unemployed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".