Decoding and Disclosure in Students-as-Partners Research: A Case Study of the Political Science Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it