Examining Optimal Weights for the Youth Assessment and Screening Instrument in North Dakota
Bibliographic record
Abstract
The purpose of deriving weights for factors is to identify and discover what items have the strongest relation to the outcome of interest to create an assessment with the best predictive validity. The current study examined and compared weighted and nonweighted models for the pre-screen of the Youth Assessment and Screening Instrument (YASI). The total sample of 6,175 youth from the state of North Dakota, United States, was divided into the calibration sample (33%) to build the models and was cross-validated across five independent subsets. The results were that no method consistently nor substantially outperformed the original method. The one exception was the risk total score produced through the logistic regression model substantially improved the predictive accuracy for violent dispositions after the initial YASI administration in the validation subsets. Therefore, it may be worth considering alternative models and warrants further exploration for the users of the scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".