Indexing, enriching, and understanding Brazilian missing person cases from data of distributed repositories on the web
Bibliographic record
Abstract
Abstract For decision making in government, it is necessary to have well-structured sources of information. In several countries, it is difficult to access government data as the information are dispersed, disconnected, and poorly structured. For this reason, this work presents a framework to gather, unify, and enrich missing person data from distributed web sources. The framework allows inserting new tasks specific to the user’s domain to improve data quality. In this study, Brazilian missing person data from non-governmental organizations (NGOs) and governmental websites were collected and semantically enriched. To enhance the understanding of the gathered missing people cases, we create interpretive models using machine learning techniques to extract knowledge and to encourage the use of standards for publishing the data that are frequently ignored by organizations, hindering analysis and decision-making on data. After the collection and semantic enrichment process, there was an increase of approximately 11% in the data present in the base. Also, the mining process evidenced the disappearance and reappearance of a person in Brazil according to several factors such as age, state initiatives, skin tone, hair colors, etc.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".