An environmental level analysis of economic correlates of child abuse in the Lower Mainland
Bibliographic record
Abstract
The question of the etiology of child abuse has received growing research attention since the early 60's. This attention has moved from a strictly individual psychopathology focus to a more systemic, environmental perspective. The determination of significant correlates of child abuse, in view of this dichotomy, holds very important policy and intervention implications. This is because as the importance of environmental factors rises, so does the "depth" or systemic nature of the intervention required. The environmental model proposes that abusive behaviour is not only a function of an individual's psychological makeup. In addition, factors outside the individual are thought to be critical components in the abuse phenomenon. These factors are a part of the social environment of the individual and thus impinge upon all individuals who live in that environment. Research into environmental correlates of child abuse is still at an early phase of development. The works of James Garbarino, Blair and Rita Justice, and Ralph Catalano, David Dooley, et al. have made progress in identifying possible significant systemic correlates, in tentative connective hypotheses, and in research approaches. Garbarino, and Catalano, Dooley, et al have focused on various community-level features of economic climate. The Justices' have focused on the importance of high levels of stress in inducing abusive behaviour. However, this work has all pertained to the American environment. There are no studies which have started replicating their seminal work in Canada. The present study does precisely this. This study tests for the existence of an association between selected correlates of the economy's ability to provide jobs and the incidence of child abuse. The selected correlates of the economy are the size of the labour force and the unemployment rate in the Greater Vancouver Metropolitan area, and the rate of income assistance receipt by employable persons for a subset of municipalities found within the Greater Vancouver Metropolitan area. These variables are aggregate monthly totals. They are correlated with a monthly incidence rate of child abuse reports drawn from the same geographical area as the income assistance statistics. The ability of a community to provide jobs is a significant feature of a community's environment, and one which may create general stress. Thus, a significant correlation was expected. The series' were manipulated using the ARIMA method of time series analysis in order to remove regular, patterned behaviour in the series'. The "prewhitened" series' were then regressed from a twelve month lead through to a twelve month lag interval. This resulted in 300 correlations. The findings were very conservative, with only 13 significant correlations. The interpretation of this was based on patterns of correlation, consistency across lags and between similar variables. There did not appear to be any consistency in the significant findings. However, regression of unprewhitened series' showed very significant correlations. This lead the researcher to the conclusion that the modelling process removed whatever features were producing the correlation. This suggests, due to the nature of the modelling process, that some regular or very subtle pattern occurs within both the economic series' and the child abuse series'. Further research is needed to determine the nature of this pattern, and the degree of actual correlation it indicates, as opposed to a simple third variable explanation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".