“I Am More than My Country of Origin”: An Arts-Based Engagement Ethnography with Racialized Newcomer Women in Canada
Bibliographic record
Abstract
Many women immigrate with the hope that they will gain new opportunities for themselves and their families, however, they often face significant challenges due to the intersectional stigmas related to their gender, immigration status, and other aspects of their social location. In this study, we sought to understand the holistic experience of racialized newcomer women to better support their integration process. Using Arts-Based Engagement Ethnography (ABEE), we employed the use of cultural probes and qualitative interviews to gain an in-depth understanding of the experience of ten newcomer women. An ethnographic analysis of this data yielded four overarching structures which include (1) identity negotiation experiences, (2) process of integration and struggles with transition, (3) resiliency practices and processes, and (4) making meaning of migration experiences. Each of these structures included several patterns. Our results demonstrate the benefits of using arts-based qualitative methods with diverse communities to collect rich and varied data that highlights the multiple social identities of participants. These results also give an in-depth look at the numerous experiences, both positive and negative, that influence the well-being of newcomer women throughout the process of migration. The implications of this research emphasize the need to continue in our efforts to reduce systemic oppression, to create a more inclusive and equitable community.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".