False allegations of abuse and neglect when parents separate
Bibliographic record
Abstract
Objective: The 1998 Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-98) is the first national study to document the rate of intentionally false allegations of abuse and neglect investigated by child welfare services in Canada. This paper provides a detailed summary of the characteristics associated with intentionally false reports of child abuse and neglect within the context of parental separation. Method: A multistage sampling design was used, first to select a representative sample of 51 child welfare ser-vice areas across Canada. Child maltreatment investigations conducted in the selected sites during the months of October–December 1998 were tracked, yielding a final sample of 7,672 child maltreatment investigations reported to child welfare authorities because of suspected child abuse or neglect. Results: Consistent with other national studies of reported child maltreatment, CIS-98 data indicate that more than one-third of maltreatment investigations are unsubstantiated, but only 4 % of all cases are considered to be intentionally fabricated. Within the subsample of cases wherein a custody or access dispute has occurred, the rate of intentionally false allegations is higher: 12%. Results of this analysis show that neglect is the most common form of intentionally fabricated maltreatment, while anonymous reporters and noncustodial parents (usually fathers) most frequently make intentionally false reports. Of the intentionally false allegations of maltreatment tracked by the
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".