My Story of a Global Art Educator: Exploring Creative Encounters with Ukrainian Vernacular Art in Postmigrant Refugee Worldmaking
Bibliographic record
Abstract
The destruction in my native Ukraine intensified my need to critically and meaningfully locate the sense of self in relation to the world and the groundbreaking events surrounding the outbreak of the Russia-Ukraine war in 2022, alongside my role as an art educator in fostering peaceful and inclusive educational environments. I turned to Global Citizenship theory and explored the globality of forced migration and the issue of war by considering the migrant identity of a global citizen from my local perspective as a Ukrainian immigrant, as well as that of Ukrainian refugees in Montreal, Canada. My main question was how selected Ukrainian refugees experience identity, belonging, and difference beyond the traditional binary and exclusionary perspectives on migration. To understand their experiences, I investigated the notion of worldmaking denizen proposed by the postmigrant analytical perspective while applying narrative inquiry methods coupled with the artistic lens of Ukrainian vernacular art, particularly Petrykivka painting. This approach facilitated a nuanced and meaningful understanding of the participants’ experiences as a transversal dialogue across differences and antagonisms. The understanding guided me in conceptualizing my globally oriented professional position through a hybrid confabulation of motif-motive—an inclusive and relational representational system that encompasses art, citizenship, and the significance of individual and community action in shaping the future.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".