All parts of the same thing: Dispatches from the Creativity Everything Lab
Bibliographic record
Abstract
We established the Creativity Everything lab at Ryerson University in 2018 as a place that would support and unlock ‘all kinds of creativity for all kinds of people’. In this article, we detail the transdisciplinary roots of our work, and outline some of our activities and the thinking behind them. As a team of researchers developing projects and experiences that embrace a wide range of creators and creative practices, we are fashioning the lab to facilitate the actions of doing and making in a range of spheres: in everyday life, professional creative practice, and in learning and research. Three case studies – our ongoing efforts at supporting learning for students, a research project on platforms for creativity, and the community outreach of the 2019 Creativity Everything FreeSchool – explore how teaching, research, events, and collaborations in multiple media intersect in a multifaceted system for relating to and engaging with creativity. Our studies suggest that creative practice-as-research helps people make connections that fuel curiosity and experimentation. We argue that engaging in multiple perspectives of the “everything” of creativity better equips our students, university, and public to reap its benefits and rewards.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".