Bibliographic record
Abstract
Wicked problems" are problems that are difficult to solve because they have a high degree of complexity, and the circumstances surrounding them are somewhat unique.Each solution gives rise to a new problem; thus, problems must be resolved over and over again without the ability to apply a commonly recognized model or blueprint (Rittel & Webber, 1973).Child abuse and neglect are wicked problems, and in the last national study conducted in Canada in 2008, more than 235,000 allegations of child maltreatment were identified (Public Health Agency of Canada, 2010).The rate of children in care in Canada is among the highest in the developed world (Thoburn, 2007), and the disproportionate rate of Indigenous children in care, particularly in Western Canada (Sinha, Trocmé, Blackstock, MacLaurin, & Fallon, 2011), is a national tragedy.The child welfare system is under intense public and media scrutiny, yet efforts to protect children and support families continue to be confronted by constraints in funding, poorly coordinated service responses, and challenges in training and retaining professionals.An even more important issue is the inadequate systemic response to the structural causes of child maltreatment, including poverty, poor housing, and racial injustice, which in the case of Indigenous children and families are related to the legacy of residential schools and other forms of colonialism.The plethora of special studies and inquiries have, at best, resulted in modest changes in service delivery or funding, and too often these are accompanied by new procedures and requirements for compliance that seem to impede rather than support best practices that both protect children and support families.Meanwhile, our search for a single best practice model in child and family welfare, like the search for the Holy Grail, continues in vain.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.514 | 0.464 |
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".