Designing for meaning : uniting creative and scholastic research through collective : practices in event design
Bibliographic record
Abstract
Practice-based creative research, also known as research-creation, exists in a tenuous position between art and academia. There has been significant, ongoing research into the role of curatorial processes, research, and public-facing events as forms of knowledge production which draw from both traditional informational methodologies and creative or artistic approaches. However, much extant work on this subject is centered on gallery and museum spaces. This paper describes a creative research project carried out by a team of graduate students at Concordia University, which aims to bring research-creation into direct, engaged conversation with more traditional forms of academic research through the research collective’s development of interdisciplinary symposia. We discuss the importance of taking a design approach, including documentation and iterative practices, in order to create an environment in which creative research and scholastic research are treated as equally important forms of knowledge production. Specifically, we detail how the idea of meaningful methods influenced our approach, and how designing for connection and embodied experience are essential to creating event spaces which facilitate interdisciplinary knowledge exchange.
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 | 0.072 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| 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".