How can students-as-partners work inform assessment?
Bibliographic record
Abstract
This fourth iteration of Voices from the Field highlights some of the many meanings and practices of assessment as faculty/academic staff, professional staff, and students define it and as they situate it in relation to students-as-partners work. The goal of this section of the journal is to offer a venue for a wide range of contributors to address important questions around and aspects of students-as-partners work without going through the intensive submission, peer-review, and revision processes. For this iteration of Voices, we invited responses to the question: “In what ways can students-as-partners work inform assessment?” Recognizing that assessment means different things in different contexts, we invited contributors to specify what definition they are working with.
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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 | 0.106 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.049 | 0.054 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".