Impactos da Inteligência Artificial nos processos de adoção
Bibliographic record
Abstract
O artigo científico aborda a morosidade nos processos de adoção no Brasil, destacando a inteligência artificial no sistema jurídico, questões éticas e expectativas de melhoria na celeridade dos processos, além da proteção psicológica das crianças e adolescentes conforme o Estatuto da Criança e do Adolescente. Também discute o acesso à justiça e a desburocratização dos processos legais. Para uma compreensão abrangente, é necessário considerar os aspectos positivos e desafios associados, incluindo implicações éticas, sociais, econômicas e legais. Os obstáculos incluem burocracia, falta de estrutura e capacitação, problemas documentais, complexidade das questões e falta de incentivo. Abordar esses problemas requer melhorias na legislação, investimento em estrutura e conscientização. O artigo vislumbra expectativas de processos mais céleres, destacando o potencial da inteligência artificial para promover o bem-estar das crianças e adolescentes e garantir sua proteção integral.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".