Storying the Terroir of Collaborative Writing: Like Wine and Food, a Unique Pairing of Mentoring Minds
Bibliographic record
Abstract
As two faculty members in a Canadian post‐secondary teacher education context, the authors inquired into their collaborative writing process initiated through an informal faculty mentoring relationship. Situating their writing in the discourses of personal practical knowledge, social constructionism, narrative inquiry, and autobiography grounds their understanding of relational writing as side‐by‐side collaborators who engage in a bodily co‐present writing process, negotiating the many nuances of text construction. By using a metaphor of carefully pairing exquisite wine with fine food, they convey the mutual co‐construction of their lived experiences that evolve through relational writing. Highlighting related literature in the areas of mentoring, writing terminology, traits of collaborators, writing process, and benefits of collaboration assists them in comparing and contrasting the literature with features unique to their own collaboration. They conclude by noting critical issues and implications regarding collaborative writing that offer insight into the importance of honoring collaborative scholarship within academic contexts.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".