Bibliographic record
Abstract
The goal to understand what factors predict child welfare service decisions may be addressed through the quantitative method of multilevel modelling (MLM). MLM provides an opportunity to examine whether child welfare decisions can be predicted by various characteristics and whether they vary by group factors, such as worker, team, department, organisation or geographical location. This quantitative method addresses data dependence through data collected at multiple levels, a specific data set structure and multiple statistical tests. Unless nested data are appropriately structured and analysed, such as in MLM, there is an increased risk of Type I errors, that is, false positives. MLM provides researchers of decision making with the opportunity to assess whether decisions are unique to certain group characteristics and the degree to which decisions vary. This article presents MLM as a method for exploring decision making through an example taken from the Canadian child welfare context, whereby clinical, child welfare worker and organisational characteristics are assessed for their relationship to decisions to transfer families to ongoing child welfare services. Results illustrate the importance of utilising MLM as a method for exploring decision making and potential future uses.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".