Building a Pedagogy of Writing Transfer Through an Undergraduate Journal Publication
Bibliographic record
Abstract
Despite effective knowledge transfer being a primary goal among post-secondary instructors, scholars, and administrators, students still have difficulty adapting their skills to novel contexts. This challenge is especially salient in writing pedagogy, where the transfer of writing-related knowledge is not guaranteed (Driscoll, 2011; Perkins & Salomon, 2012). To investigate possibilities for catalyzing writing transfer, this paper reports on a collaborative autoethnography project (Chang et al., 2013) carried out by two undergraduate students and a faculty member based at a large Ontario university. Their experiences on the editorial team of a first-year writing journal provide insight into how mentorship within journal environments can contribute to post-secondary students’ literacy development, and, concurrently, help them to transfer what they know to new contexts. We consider how similar learning opportunities may contribute to undergraduate skill development outside traditional classroom contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".