Dealing with Expert Uncertainty in Assigning Motor Competence Scores: New Dimensions of Validity Evidence
Bibliographic record
Abstract
We discuss a new dimension of quantitative validity evidence that considers subjective rater uncertainty in assigning item scores via the use of rater-elicited confidence scores and the mathematical apparatus of random-variable-valued measurements. We examine implications for inter-rater reliability and construct validity for 16 raters who assessed fundamental motor skills using an augmented version of the Test of Gross Motor Development-3rd Edition to capture their subjective confidence in assigning definitive binary scores for each item. Analysis of the augmented test revealed substantial subjective rater uncertainty in 9 of 50 components, considerable variation in subjective uncertainty across equally trained raters, and a tendency to produce higher total and subscale scores when using only a traditional binary scoring system. These findings suggest that there are important subjective differences in how different raters interpret and apply common criteria for assessing fundamental motor skills and highlight the need for centralized training of raters before clinical application.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".