Questioning the Code of Rights and Responsibilities
Bibliographic record
Abstract
University policies significantly affect university students’ academic lives. Concerningly, students do not engage with university policies even when they are directly provided (Jordan, 2001). This intrinsic case study bricolage employs Critical Pedagogy as its theoretical framework to explore how six students used Question Formulation Technique (QFT’) to engage and interact with their university’s Code of Rights and Responsibilities (the Code’) and each other in two peer groups of three students each in separate sessions. Data sources include participant-generated questions, an observation sheet, and individual post-session questionnaires. Both participant groups had difficulty self-organizing during QFT and engaging with the Code due to a lack of prior knowledge of its terms, structure, and origins. A modified Bloom’s Taxonomy (Anderson & Krathwohl, 2001) and a Question Types and Purposes Rubric analyzed the generated questions to determine the questions’ cognitive levels and objectives. Data triangulation revealed that QFT allowed participants to dialogue with each other about the Code, and that most participants recognized their knowledge gaps concerning the Code and expressed intention to use QFT in the future unless they already used a preferred questioning technique. This study recommends providing university students with policy literacy instructional interventions and using QFT as a needs assessment tool.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".