Doctoral Student Reading and Writing: Making Our Processes Visible
Bibliographic record
Abstract
Reading and writing are core components of what it means to be a doctoral student. Although reading and writing are known to be discursive, socialized practices, doctoral programs often focus on the output of these practices and position reading and writing as generic, universal skills. Through collaborative self-study, we sought to examine our reading and writing processes and see what we could learn as doctoral students by making these processes visible. From our analysis, we discovered that understanding our reading and writing processes enabled us to use effective reading and writing strategies; revealed the benefits of blurring personal-professional boundaries; and contributed to shaping our identity as emerging scholars. We conclude that supporting doctoral students to examine their personalized reading and writing processes, opposed to solely focusing on output, can support them to look inward, locate meaning within themselves, and recognize the multiplicity in what it means to read and write at the doctoral level.
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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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".