Livras: An App to Help Women at Risk
Bibliographic record
Abstract
Gender-based violence affects millions of women worldwide, transcending cultural, economic, and social boundaries.According to an ONU survey, home is the most dangerous place.In a large number of cases, even with the possibility of local telephone numbers for help, at-risk women cannot make a call simply because the aggressor is close to them.Considering the accessibility of communication for the deaf, there are tools to teach American Sign Language (ALS), which is also used in countries other than the United States.The Signal for Help was created by the Canadian Women's Foundation, allowing women to silently send an SOS.The idea presented in this work combines a fake app to teach sign language with a way to use this signal without the possible note of any neighbor aggressor.This is a silent and effective request to help at-risk women.After implementing the first version, it was tested by users who suggested significant improvements, and a second version was developed.This version was also sent to users for experimentation, and an update to the current version is presented here.Thus, there are useful functionalities in the application that fulfill the needs of daily use, such as access by voice activation, login with facial recognition, interface with contrasting colors, and the possibility of setting the language of the screen texts and menu words, among others.The geographical location of the woman sending the signal helps in her location, allows quick contact with the protection service in the region, and helps in identifying areas of greater risk (geographic tracking map) for the regional public security authorities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".