Connecting Child Welfare and Immigration Systems: The Role of CWICE
Bibliographic record
Abstract
The report outlines how the Child Welfare Immigration Centre of Excellence (CWICE) bridges the gap between child welfare and immigration by supporting children, youth, and families with immigration issues. Through participatory systems mapping (PSM), we gained insights from workers’ perspectives on how CWICE interacts with both child welfare and immigration systems. The systems map (see Appendix 2) visually represents the support systems for child welfare considerations at entry ports, highlighting CWICE's role in connecting these systems to build holistic support and safety for families and communities. The challenges and benefits of CWICE's involvement are explored through worker interviews. Participants acknowledged the expertise of CWICE workers in navigating the complex immigration process, while indicating that challenges like worker turnover and the lack of clarity in designated representatives can complicate circumstances for families. The report emphasizes collaboration and training as factors leading to more effective services, as well as the need for greater awareness of CWICE's services among settlement agencies to provide comprehensive support. Lastly, we recommend future research initiatives to better understand unaccompanied children's experiences in various child welfare systems across Canada. The report concludes by encouraging continued innovation and proactive collaboration in the child welfare sector to increase the safety and well-being of families and children dealing with immigration issues.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".