Placement in out of home care during investigations in Ontario in 2018
Bibliographic record
Abstract
The Ontario Incidence Study of Reported Child Abuse and Neglect 2018 (OIS-2018)1 is the sixth provincial study to examine the incidence of reported child maltreatment and the characteristics of children and families investigated by child welfare authorities. [...] This Information Sheet compares investigations in which there is an out of home placement during the investigation to those where there is not a placement for the following factors: the child demographics, child functioning issues, primary caregiver characteristics, family household characteristics, and characteristics of the investigation. [...] Detailed findings comparing investigations in which there is an out of home placement during the investigation to those where there is not a placement are presented in Table 1 in the appendix. [...] Investigations where the primary reason for investigation was risk only represented a slightly higher proportion of cases resulting in a placement (38.9%) compared to those where there was no placement (37.5%). [...] The worker could decide that the child was at risk of future maltreatment (confirmed risk), that the child was not at risk of future maltreatment (unfounded risk), or that the future risk of maltreatment was unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".