Desinard Alves, joint with Christopher Timmins Social Exclusion and the Two-Tiered Healthcare System of BrazilSocial Exclusion And the Two-Tiered
Bibliographic record
Abstract
In Brazil there exists a two-tiered system of healthcare acces. Those with sufficient means have access to a private system of healthcare that provides quality treatment on demand, while the remainder of the country relies on an overburdened system of public clinics and hospitals. Households survey data are used to determine wich soiodemographic groups rely on this public healthcare system. Current demographic trends suggest that the public healthcare infrastructure will become more and more heavily used in the coming decades. A stylized model of healthcare choice is estimated, and its parameters are used to conduct counterfactual simulations of the welfare implications of this increased congestion, and of policies to offset it, like private healthcare subsidies. The authors gratefully acknowledge the financial support of the Inter-American Development Bank. Ignez Tristao and Fabiana Tito provided outstanding research assistance. Helpful comments and advice were received from Paul Schultz, Chris Uldry and all participants of the Yale University Development Lunch and the IDB seminars associated with this research initiative. 1 1
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".