Decoding and Disclosure in Students-as-Partners Research: A Case Study of the Political Science Literature Review
Bibliographic record
Abstract
The Decoding the Disciplines (DtD) methodology has been used to study bottlenecks to student learning in a range of disciplines. The DtD interview process involves conversations between faculty regarding disciplinary practices. This article analyzes the use of the DtD approach in a student-faculty partnership to explore questions about disciplinary learning in political science. The research team compared how faculty and two cohorts of undergraduates decode a specific disciplinary bottleneck—the task of writing a literature review in political science. Results from the interviews reveal fundamental differences in how faculty and undergraduates conduct literature reviews in this discipline, including a troubling disjuncture as undergraduates become more expert in this process. Because the research team included both students and faculty, we also explore issues of disclosure and power in student-faculty partnerships in SoTL research.
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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 | 0.098 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".