Do outlier assessors provide useful narrative comments?
Bibliographic record
Abstract
Introduction Outlier stringent/lenient and range-restricted assessors provide low utility assessment scores, reflecting assessor tendency more than learner performance. The quality of narrative comments generated by these assessors remains unknown.Methods End-of-shift assessments from an academic emergency department were scored using the Quality of Assessment of Learning (QuAL) score. The mean Qual score was calculated for each assessor. Stringency/leniency and range restriction were quantified using the mean-delta method and standard deviation of awarded scores respectively. Outlier vs. non-outlier assessor mean QuAL scores were compared using T-tests. Linear regression was conducted with mean QuAL score as the dependent variable, and assessor range-restriction as the independent variable.Results 2034 assessments were completed by 81 assessors. We identified 20 outlier assessors (10 stringent, 10 lenient). Assessor mean QuAL scores ranged from 1.2 to 5 (mean = 3.9, SD = 0.9). Mean QuAL score of non-outliers (4.0) was significantly higher than outlier lenient assessors (3.1, p = 0.02) but not different from outlier stringent assessors (3.8, p = 0.5). Range restriction was negatively correlated with mean QuAL scores (p = 0.003, R2=0.11)Conclusions Outlier lenient and range-restricted assessors provided lower-than-average quality narrative assessments. These assessors, where both numeric and narrative assessments are of limited utility, are ideal candidates for targeted interventions to improve assessment quality.
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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 | 0.021 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".