Bibliographic record
Abstract
Art and design practices and creative writing have long enjoyed a symbiotic, mutually inspiring relationship—as they do in my current position at OCAD University, the first, largest and most comprehensive art, design and media university in Canada—so it comes as no surprise that more and more post-secondary art and design institutions see Creative Writing programs as a natural fit to educate creative problem-solvers. But beyond recognizing synergies of practice and process in a postsecondary arts context, how might we, as educators, foster true interdisciplinarity and an ongoing cross-pollination of creative modes of production between visual artists, designers and writers? How might these synergies be explored and expanded upon in the classroom, the curriculum and perhaps most importantly, in the community? How do we create space and opportunities to encourage writing students to produce language-based work that explores visual, spatial, tactile, digital and performative elements? This chapter will examine methodologies that foster exploration in interdisciplinarity, outlining projects and practices undertaken in the first four years of OCADU’s Creative Writing program, including in-class experiences and exercises, public projects, curricular intersections and student-led events and publications, all of which encourage writing that seeks new spaces for text and engages with the precepts, materials and processes of art and design practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".