COVID19 Impact on Child Maltreatment: Evidence from Abuse and Neglect Investigations in Texas
Bibliographic record
Abstract
\nCOVID-19 has brought challenges to the society in various aspects, as one of the most vulnerable members of society, children’s lives have also been significantly affected by it. This study aims to address the child maltreatment impacted by the COVID-19 pandemic. Leveraging county level data sets from Texas Department of Family and Protective Services and United States Census Bureau, XGBoost method and fixed effect model was used to investigate the most important economic, demographic, and social factors. It is found that population of 16-year-old or over, the rental vacancy rate, the population of 16-year-old or over that commutes to work by walking, and population of 16-year-old or over that works in agriculture, forestry, fishing and hunting, and mining industry is positively associated with total number of child maltreatment cases. On the other hand, the total population in labor force, the female population not in labor force, the average of public cash assistance (in dollars), the average time (in minutes) commuting to work, and the population whose household contains 2 units/rooms are negatively associated with total number of child maltreatment cases. Also, a single-difference model was implemented to identify that the COVID-19 pandemic was associated with 7.6% increase in total number of child maltreatment cases.\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".