Identification of Ineffective Team Members Using Normalized Peer Ratings
Bibliographic record
Abstract
Peer assessments are used in the senior process design course at the University of New Brunswick to determine individual grades, reduce free riding and improve team dynamics. The results of the monthly surveys are shared with students as formative feedback on their performance and used to calculate the relative performance of team members by dividing the overall average score of each student by the team average. This study examined whether the resulting normalized peer ratings could be used to identify ineffective team members. We found that the normalized ratings of effective team members follow the normal distribution with a mean of unity and a standard deviation of about 0.04. The small monthly variations in the standard deviation of the distribution are not statistically significant indicating that the characteristics of the distribution are time-invariant in our course. Students who obtain normalized ratings less than 0.9 are generally ineffective team members and their ratings should not be used in the calculation of the team average to avoid inflating the normalized ratings of their teammates.
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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 | 0.001 |
| 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".