Bridging Home: Building Relationships between Immigrant and Long-Time Resident Youth
Bibliographic record
Abstract
Background There is rising evidence that relationships that bridge between immigrants and long-time residents are critical to immigrant integration and to the overall heath of communities. The processes by which this bridging social capital is built are not well understood. Schools in new immigrant destinations, as spaces in which diverse youth come together, provide a unique opportunity to examine how immigrant and long-time resident youth connect to each other and build relationships. Purpose This article examines the processes of building relationships between immigrant and long-time resident youth and explores the meaning and consequences of these processes for the individuals involved. The article further suggests ways in which schools might adopt strategies to promote personal interaction, cooperative action, and collective identification to aid in the development of these relationships. Setting Lewiston, Maine is the setting of this study. Between February 2001 and May 2003, 1,200 Somalis arrived in Lewiston, a town of 35,690 people, 97.3% of whom were White at the time of the 2000 Census. Research Design Using the methodology of portraiture, this study examines, as an exemplary case, one relationship between two students: a Somali immigrant, and a White longtime resident. Portraiture is a methodology built on relationships, which mirrors the theoretical issues under investigation. Findings/Results This study provides new insights into how bridging relationships are built. The participants capitalized on the common space of their new immigrant destination school to transform casual personal interactions into a bridging relationship based on collective identification. Through dialogue, particularly about race, they challenged each other and themselves, and each came to understand the other in new ways; they also became invested in each other and dependent on each other to grow and to understand themselves and their places in a changing town. Conclusions/Recommendations The research identifies processes of personal interaction, cooperative action, and collective identification as central to the building of bridging relationships. It also reveals the necessity of a focus on race when researching, analyzing, or cultivating these relationships. Lessons for educators and schools seeking to foster relationships between immigrant and long-time resident youth include engaging students in direct dialogue about race and cultivating skills in empathetic storytelling and listening in order to “double-think,” or receive a counter-story.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".