Testing Inter-Rater Reliability in Rubrics for Large Scale Undergraduate Independent Projects
Bibliographic record
Abstract
This work outlines the process of testinginter-rater reliability in rubrics for large scaleundergraduate independent projects; more specifically,the thesis program within the Division of EngineeringScience at the University of Toronto, in which 200students work with over 100 supervisors on anindependent research project. Over the last few years,rubrics have been developed to both guide the students inthe creation of their thesis deliverables, and to improvethe consistency of supervisor assessment. To examineinter-rater reliability, 12 final thesis reports wereassessed using the course rubric by the two generalistexperts, who have worked extensively with the thesiscourse and designed the rubrics, alongside the projectsupervisor. We found substantial agreement between thetwo generalist experts, but only fair agreement betweenthe generalist experts and the supervisors, suggesting thatwhile the rubric does help towards developing a commonset of expectations, there may be other aspects of thesupervisor’s assessment practice that need to beconsidered.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 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".