(un) Disciplining the<scp>n</scp>urse<scp>w</scp>riter: doctoral nursing students' perspective on writing capacity
Bibliographic record
Abstract
In this article, we offer a perspective into how Canadian doctoral nursing students' writing capacity is mentored and, as a result, we argue is disciplined. We do this by sharing our own disciplinary and interdisciplinary experiences of writing with, for and about nurses. We locate our experiences within a broader discourse that suggests doctoral (nursing) students be prepared as stewards of the (nursing) discipline. We draw attention to tensions and effects of writing within (nursing) disciplinary boundaries. We argue that traditional approaches to developing nurses' writing capacity in doctoral programs both shepherds and excludes emerging scholarly voices, and we present some examples to illustrate this dual role. We ask our nurse colleagues to consider for whom nurses write, offering an argument that nurses' writing must ultimately improve patient care and thus would benefit from multiple voices in writing.
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How this classification was reachedexpand
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".