Bibliographic record
Abstract
How and why do we use writing in the contemporary Fine Arts classroom? What are the expanded possibilities of composition as a pedagogical tool, especially considering the mutability of writing and its already ambiguous role in interdisciplinary creative practice? This paper uses our year-long foundational course at Concordia University, with an enrolment of 900 students from across the Faculty of Fine Arts (including design and performance-based disciplines), as a case study to explore these questions. The class, ‘Keywords: Reading the Arts Across the Disciplines’, employs a roster of ever-changing keywords – such as sense(s), failure, care, and the future — as sites to develop interdisciplinary thought-work and artwork. Drawing on problem-posing pedagogy and critical theory, we think of keywords as knots that invite us into an exploration of tangles. ‘Keywords’ (the course) is also a knot, presenting us with a series of intertwined (and impossible) questions. What writing skills do all 900 of our students need? How will they use writing in their future practices? This paper follows a series of keywords that we have explored in class, offering practices and reflections at the intersection of writing, interdisciplinarity, and creative arts pedagogy, all of which centre composition as an embodied practice.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".