Bibliographic record
Abstract
While playfulness is important to graduate writing to shift students into new ways of thinking about their research, a key obstacle to having fun is writing anxiety. Writing is emotional, and despite a growing field of research that attests to this, emotions are often not explicitly recognized as part of the graduate student writing journey. Many students experience writing anxiety, particularly when receiving feedback on dissertations or papers for publication. Feedback on writing-in-progress is crucial to meeting disciplinary expectations and developing a scholarly identity for the writer. Yet many students are unable to cope with the emotions generated by criticism of their writing. This paper presents pedagogical strategies—free-writing, negotiating negative internal dialogue, and using objects to externalize feelings—to help students navigate their emotions, while recognizing the broader discursive context within which graduate writing takes place. Reflections on the pedagogical strategies from nineteen Masters and PhD students attending a course, Graduate Research Writing, were used to illustrate student experiences over the semester. The pedagogical strategies helped students to recognize their emotions, to make decisions about their emotional reactions and to develop agency in the way they responded to critical feedback. By acknowledging the emotional nature of writing, students are more open to creativity, originality, and imagination.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".