Sustaining life on earth: An arts-based research exploration of collective lived experiences of COVID-19
Bibliographic record
Abstract
This article presents the philosophy, innovative methods, and final aesthetic synthesis of a collaborative arts-based research project about the lived experience of COVID-19. The project was initiated in 2020 and completed in 2022. Nineteen international arts-based research scholars participated as co-researchers, submitting their arts-based and narrative responses to the project. The six-member core research team guiding the project collected and organized the submissions while simultaneously entering into immersive, iterative, dynamic, arts-based data generation, dialogic analytic and syntheses processes with co-researchers, and each other. Materials-discursive analytic processes, arts-based responses, sensorial coding, intersubjective dialogues, and arts-based assemblages conducted iteratively throughout the project. The performative result captured the sensory, embodied, and emotional experiences of the evolving stages of the pandemic as identified by and resonant with the co-researchers and multiple audiences. These stages were identified during the project by the co-researchers as: initial anxiety and panic; reflection and creativity; and resilience. The final synthesis of the project is an arts-based and performative piece using video and interactive gallery venues representative of these stages.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".