Interrogating Conflicting Narratives of Writing in the Academy: A Call for Research
Bibliographic record
Abstract
A safe haven in an often unsafe place: I would use this metaphor to describe the space writing studies and a university writing centre have offered me, as I’ve attempted to find my own place as a feminist in the academy. I feel these two things are my rocks. They are firm, solid places for me to reside amongst the challenges I’ve experienced as a writer. The reasons for my struggles with writing for academic purposes are difficult to pinpoint. Some would say they stem from my lack of literacy, hinting that laziness could be a culprit. Others might suggest they are connected to my subjective identity as a first-generation, female university student. Or others might take the discussion of subjective identity a bit further, arguing that my identity as a feminist, and my determination to bring my feminist politics into my academic work explain these challenges.
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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 | 0.067 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.038 | 0.088 |
| Scholarly communication | 0.051 | 0.054 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".