DnA: Design and Art Research Methodologies – Understanding the Way We Work
Bibliographic record
Abstract
DnA, our work, Design and Art, while each hold to their distinctions and are often understood as separate fields of creative practice and enquiry they are also widely acknowledged as inherently intertwined. The DnA project was initiated by a keen desire to identify the intrinsic connections amongst peers. This is a generative research study set in parallel to the way we work, the way we understand our work and the way our work evolves. It has been built on the premise that the acts of listening, questioning and riposte might help to unravel the complex matrix that makes up research within the creative fields. Our aim is to make our form of research more easily understood between ourselves and in turn to our peers situated in other fields of study or context. The DnA project responds to a rich and vibrant university commons: a collective of individuals working with a plethora of different mediums, distinct material practices and methodologies who form a critical creative mass. It is an emergent enquiry connected to Emily Carr University of Art + Design in Vancouver, Canada. Through Individual Practitioner Interviews, construction of Responsive Spaces and Dissemination Events the DnA team aims to investigate the unique aspects of individual and collaborative practices; to document design and art methodologies at work amongst peers. Treating topic and resource as native, the DnA project frames creative practice in research through the active use of creative research methodologies. It is a reflexive, generative effort that is still in process. This paper frames work done to date. The project has several goals: To identify convergence amongst disciplines; to facilitate accessible collaborative expression; to promote further interest in emergent topical conversations centered on research; to generate and promote new, (open - positive – collaborative) works and methods that reflect the strengths within each of the creative disciplines represented in the University and finally to develop emergent strategies for conversation and collaboration.
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 | 0.074 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.035 | 0.026 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".