Child Abuse and Neglect, Emotion Processing, Demographic Factors
Bibliographic record
Abstract
In 2014, a study was published that examined the relationship between childhood abuse and neglect and emotion processing in adulthood. The results indicated a correlation between child abuse and decreased emotion processing. However, this previous study is limited to a specific time period, geographical location, and socioeconomic class. To explore the generalizability of this study, I propose conducting a similar study but with slightly different demographic characteristics: in the year 2022, in Canada, and with participants from higher socioeconomic status. My proposed methodology includes several measures that account for the variables of interest – child abuse, emotion processing, depression, anxiety, and PTSD as well as demographics. My study will use a regression model to analyze the results to compare the findings to that of the original study. The purpose of my study is to test whether or not the original findings about child abuse and emotion processing are generalizable in the context of other demographic factors. My proposed study will encourage further culturally-based research on this topic.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".