THE CRIMINALIZATION OF STALKING UNDER LAW Nº 14.132/2021: CHALLENGES AND ADVANCES IN THE PREVENTION OF GENDER-BASED VIOLENCE AND THE PROTECTION OF VICTIMS
Bibliographic record
Abstract
This study investigates the crime of stalking as a form of obsessive and compulsive harassment, analyzing its connection to gender-based violence and its criminalization in Brazil under Law No. 14.132/2021. The primary objective is to examine how this legislation contributes to the prevention of gender-based violence, particularly femicide, and to identify challenges in its practical application. The research adopts a deductive method, with data collection based on a bibliographic and documentary review, prioritizing high-impact national and international studies published in the last three years. Data analysis employs a legal hermeneutic approach, grounded in applicable legal doctrine and normative frameworks, and includes a comparative mapping of international legislations and practices related to stalking, highlighting experiences in the United Kingdom, Canada, and Sweden. The study also analyzes specific cases illustrating various forms of stalking and their repercussions in contexts of gender-based violence. The findings reveal that the inclusion of stalking as a criminal offense under Article 147-A of the Brazilian Penal Code represented a significant advancement, enhancing victim protection and addressing gaps in previous legislation. However, significant challenges remain, such as the limited scope of penalties and the absence of immediate protective measures, particularly in cases of cyberstalking. The research concludes that, in addition to improving legislation, it is crucial to implement public policies that promote social awareness, strengthen support networks, and train legal professionals to ensure the effective application of victim protection measures. The criminalization of stalking, in addition to preventing more severe crimes such as femicide, proves to be an indispensable instrument for safeguarding women's rights and combating gender-based violence
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".