Randomised impact evaluation of a CBT-based intervention to foster socioemotional skills in vulnerable youth in Brazil
Bibliographic record
Abstract
In this experiment, we evaluate an evidence-backed, low-cost intervention to improve academic performance and reduce risk-behaviour through the development of socioemo- tional skills amongst vulnerable children – those who are most at-risk of being victims and/or perpetrators of violence. We will conduct a Cluster-randomised Trial (CRT) at school level in two municipalities in Brazil to evaluate the SEJA intervention that is based on successful experiences conducted in Chicago, Liberia and Canada. SEJA has low direct costs and is scalable when compared to similar interventions. The program has been designed to leverage municipalities’ existing personnel and infrastructure, making it ideal for implementation in low and medium income countries. We will estimate the interventions’ causal impacts on short and long-term outcomes. On the short-term, we look at outcomes such as socioemotional skills, academic performance, school frequency and enrolment in high school. The longitudinal design of our study allows us to conduct follow-up rounds of survey and administrative data collection to estimate causal impacts on long-term outcomes, such as criminal sanctions, victimisation, other self-reported vul- nerabilities, participation in anti-poverty programs and labour market outcomes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".