Community-Based Art Education in a Pandemic World
Bibliographic record
Abstract
Art educators have faced significant challenges in reaching their students during the COVID-19 pandemic, and this challenge has been doubly difficult in community art schools where participation in classrooms is voluntary and financial precarity is the norm. In this chapter, teachers and administrators from the Pointe-Saint-Charles Art School in Montreal, Quebec, will discuss how the pandemic has affected and changed our community art school, which aims to offer affordable and free high-quality art instruction to artists of all ages and social, linguistic and economic backgrounds. Considering this, we will describe the challenges and successes of adapting our in-person art school to meet restrictions that required physical isolation, and societal conditions that required re-thinking the role of art in the lives of our students. We will discuss how our mission to build an “art-home”, meaning a place where artists of all levels and interests can come and develop their practice and share their insights in a community, guided our decision-making process, leading to adapting and creating in-person, online and informal programming, including exhibitions, outdoor arts activities, online classes and the launching of a community art therapy program. We will also consider how lockdowns challenged the administrative structure of the school, and how the pressure of the pandemic required working through competing visions and pressures for our staff members. We will conclude with some greater lessons we have gleaned from these challenges, and how these will implement the vision of the school going forward.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".