Tooling up the Multi: Paying Attention to Digital Writing Projects at the Writing Centre
Bibliographic record
Abstract
With increasing regularity over the last decade, Canadian undergraduate students are being tasked with digital writing projects (DWPs), including wikis, blogs, video and audio essays, websites, and social media engagements. Currently, Canadian writing centres are silent about how DWPs are or might be supported within writing centre programming. To initiate the discussion, we asked our 2019 CWCA/ACCR conference workshop participants to consider ways of supporting a DWP in the writing centre. Our goal in the workshop, as well as in this paper, is to reflect on the ways writing centres are and can be supporting students working on digital writing projects. Workshop participants’ questions reveal several areas of attention beyond additional technology, including resources, pedagogical approaches, and writing centre programming and spatial design. Using this paper as an extension of our workshop, we examine the literature on digital and multimodal writing (which is largely American). It is in this literature that we find implications for building writing centre supports, involving both programs and spaces that foster student efforts to recognize media/modal affordances, develop and engage in design thinking, and build self-efficacious beliefs.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".