The marginalization of Roma children & the importance of arts-based education to engage learning
Bibliographic record
Abstract
Many Roma children from the EU coming to Canada as refugees have been denied a consistent education and many suffer gaps in their learning or have not had the opportunity to receive any education at all. These circumstances are mainly due to discriminating and oppressive behaviours that have historically prevailed and exist in contemporary society. In considering the difficulty that Roma children have with education, when they arrive as refugees into Canadian schools, it is imperative that Roma children be given an opportunity to access and complete an education in an environment that is supportive, free of discrimination and sensitive to their needs as learners. \nMy research examines the role of visual art as part of an arts-based education program as a means through which Roma children are more likely to experience success with school by participating in an educational model that is engaging and supportive of their cultural ways of knowing. \n\tThis paper is a case study, grounded in critical theory, into “best practices” in education that engage marginalized Roma children with learning. The study is framed around three research questions: What is distinctly problematic for Roma children in traditional school settings? How can the arts, and art education in particular engage marginalized Roma children with learning? How can Romani arts and culture be integrated into a curriculum that works to dispel discrimination and oppression of marginalized Roma children? \n\tThe study is informed by interviews with a teacher working within a Canadian educational program for refugee children, families and board members of the Toronto Roma Community Centre, as well as my own personal observations and experiences. \nWhile I have determined that arts-based education is engaging for Roma children, the bigger question that has emerged is, “How can we use arts-based education to enhance the curricular lives and school success of the Roma, a culture of exclusion?” The answer lies in acknowledging that factors such as trust, personal connection with the teacher, parental involvement, First language acquisition, refugee status, cultural preservation, and integration, play a critical role in the educational success of Roma children.
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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 | 0.002 | 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 | 0.012 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".