The Violent Pandemic: Domestic Abuse in Mexico and Argentina
Bibliographic record
Abstract
Angel Salgado-Morales is a double major in Accounting and Spanish and will be graduating fall 2023. Accounting is my main focus and the information will be very useful because I plan to start my own business one day, so I love learning about the way business works. My Spanish major is interesting because I am a native speaker but, I don’t know much about the history of Latin-American. I have enjoyed learning the history in Spanish. In my spare time I like to spend time with my cat AJ, I also enjoy being an active member of my church. My plans for the next year are to work full time in an accounting position, either with the company I intern with or a different firm. The COVID-19 Pandemic has forced everyone inside for safety concerns. Unfortunately, for those who face domestic abuse, this safety precaution has posed a different kind of threat, specifically for women in Latin America. This presentation addresses certain factors and trends in Latin America society that have contributed to the increase in domestic violence during the pandemic lockdowns, with a special focus on comparing Argentina and Mexico. There are many factors that contribute to domestic violence, but I will focus on three that have been affected by the COVID-19 lockdowns. These include economic insecurity, social acceptance of violence against women, and alcohol abuse. During the COVID-19 lockdowns, the number of cases has increased substantially by a quarter globally. With the use of academic journals and statistical analyses, the different factors were each measured from Latin American countries throughout the lockdowns. I will compare how Argentina and Mexico reacted to and handled the threat of COVID and how that impacts each one of the three points. The information is constantly being updated as more research is conducted. Argentina and Mexico both saw increased reports to their respective hotlines, but Argentina’s hotlines dealt with more psychological and emotional abuse in contrast to Mexico’s who received more physical abuse reports. Mexico has begun to implement intuitions in almost every state to combat the violence, and Argentina has passed new laws has a social movement bringing awareness to the issue. The goal of the study is to bring awareness to the abuse of women, specifically in Latin America, experienced during the COVID-19 lockdowns. By bringing awareness to this worldwide issue, we can start to demand a change in the treatment of women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".