Capture-recapture to estimate the number of street children in a city in Brazil
Bibliographic record
Abstract
BACKGROUND: Street children are an increasing problem in Latin America. It is however difficult to estimate the number of children in the street as this is a highly mobile population. AIMS: To estimate the number of street children in Aracaju, northeast Brazil, and describe the characteristics of this population. METHODS: Three independent lists of street children were constructed from a non-governmental organisation and cross-sectional surveys. The number of street children was estimated using the capture-recapture method. The characteristics of the children were recorded during the surveys. RESULTS: The estimated number of street children was 1456. The estimated number of street children before these surveys was 526, although non-official estimates suggested that there was a much larger population. Most street children are male, maintain contact with their families, and are attending school. Children contribute to the family budget a weekly average of R21.2 dollars (4.25 pounds sterling, 6.0 euros, US7.5 dollars) for boys and R17.7 dollars(3.55 pounds sterling, 5.0 euros, US6.3 dollars) for girls. CONCLUSION: Street children of Aracaju have similar characteristics to street children from other cities in Brazil. The capture-recapture method could be a useful method to estimate the size of this highly mobile population. The major advantage of the method is its reproducibility, which makes it more acceptable than estimates from interested parties.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".