Situating the Self Through Sketching: First Year Doctoral Students Finding Their Way
Bibliographic record
Abstract
As part of a two-semester advanced research methodology course, five PhD students completed three sketches—beginning, middle, and end of course—to represent how they were thinking and feeling about themselves as doctoral students as they progressed through their first year. They also wrote reflections to complement the sketches and shared their sketches in class. At the end of the course, the students co-led a study with their two instructors to examine their learnings and understandings gained from sketching. The students wrote individual narratives about their experiences and the instructors wrote narratives about how sketching was part of their pedagogical thinking. The analysis revealed seven student themes, including positive outcomes and challenges associated with sketching. One positive outcome was that students felt empowered as the sketches gave them the opportunity to reflect back on their progress, both cognitively and emotionally, over their first year of doctoral studies. Sketching itself was a challenge for some, but all felt that it was a powerful experience. Sketching provided the instructors with insights they might not have gotten through words alone, enhancing their sense of teaching and learning, and gave them valuable information to support the students.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".