Material School Landscape Through the Eyes of Recent Refugee-Background Children and Parents: A Photovoice Study
Bibliographic record
Abstract
Background or Context: This visual ethnographic case study is part of a larger research project (2019–2023) that explores the multiplicities and complexities related to language, literacy, culture, and educational disparities among recently arrived refugees during their migration and resettlement experiences. The research took place in two adjacent multiethnic neighborhoods in a northeastern U.S. metropolitan area, where residents had limited or no access to technology, such as personal computers and home Wi-Fi. These neighborhoods were heavily populated by Bhutanese, Burmese, Karen, Sudanese, and Somali refugee arrivals. Purpose, Objective, Research Question, or Focus of Study: The purpose of this study is to investigate the perceptions of the material school landscape held by recent refugee-background parents and children in the United States. Two interrelated research questions were addressed in this study: (1) How did recent refugee-background parents and children make sense of their material surroundings in the school landscape? (2) How did they perceive the material school landscape in relation to their school practices pre- and post-resettlement? Research Design: This visual ethnographic case study is part of a larger research project (2019–2023) that incorporated community-based participatory research and ethnographic methods. Specifically, it employed photovoice to examine how recent refugee-background parents and children in the United States perceive the material school landscape. The study utilized narratives, visual materials, and photo-elicited interviews to capture their perspectives and experiences. Conclusions or Recommendations: This study demonstrates that the material school landscape is a site for cultural, academic, and social adaptation and integration for refugee-background parents and children. This landscape has evolved into a semiotic moment, capturing the essence of temporal, spatial, and individual mobility through its physical artifacts. The material school landscape works as an intricate temporal-spatial-material semiotic assemblage, shaping meaning-making with multifaceted layers in and through their practices. This study highlights that navigating this assemblage requires support dynamics from both nonhuman agents like the material school landscape itself and human agents like educators, school leaders, and the broader 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".