Imagining a Post-Pandemic Reality through an Arts-based Methodological Framework
Bibliographic record
Abstract
The COVID-19 pandemic exacerbated the precarity of artistic livelihoods across the arts and culture sector. With efforts to repair past harms and reimagine more equitable futures comes the need to center the lived experiences of artists in research and policy development. Sustainable pARTnerships: Collaboration and Reciprocity in Creative Cities is a participatory arts-based research initiative that brings together the cultural and academic sectors to jointly imagine futures in which artists can thrive. As part of this initiative, five researchers and five commissioned Toronto-based artists representative of a range of disciplines collaboratively documented challenges and potential next steps. This collaboration was framed with the methodologies of crystallization, crystal-scaping, and photovoice. Our objective is to artfully integrate artistic voices into the practice of knowledge creation and map out policy pathways for institutions and the community to create longer-term relationships built on equity and reciprocity. The visibility of local artists’ pandemic experience targets a broad public, including arts training institutes, policymakers, and academics and heightens the call for connection, conversation, and change.
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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 | 0.053 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.017 | 0.100 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".