Immigrant Volunteering And Integration
Bibliographic record
Abstract
This three-paper dissertation aims to evaluate whether, how, and the extent to which immigrants' volunteering affects their integration into the host country. I approach the thesis with a conceptual examination built on social theories (Paper 1), a quantitative analysis conducted with Canadian data (Paper 2), and a qualitative exploration based on 24 interviews conducted in Philadelphia (U.S.) (Paper 3). For Paper 1, I find that while existing social theories may explain ways volunteer activities positively influence the relationship between immigrants and nonimmigrants, they do not account for additional barriers newcomers experience during resettlement. Using an intersectional approach, I expand existing knowledge on immigrant-nonimmigrant relations by exploring volunteering as support that improves immigrant integration. In Paper 2, I use Canadian national survey data to examine three dimensions of immigrant integration: professional, psychosocial, and political. General volunteering is not significantly related to integration; however, there is a relationship between dimensions of integration and where immigrants choose to volunteer. Thus, the relationship between immigrant integration and volunteering is nuanced; it matters where immigrants decide to volunteer. I expand on this in Paper 3 by analyzing 24 semi-structured interviews to explore differences in social-integration experiences and perceptions of social integration between immigrant volunteers and nonvolunteers in Philadelphia. While past studies assert that volunteering increases feelings of social integration, this empirical study offers a comparative perspective between immigrants who volunteer and those who do not. The findings suggest that immigrant volunteers build a stronger sense of agency in their social integration journeys through contributions to society. Most nonvolunteering participants achieve similar yet minor benefits by engaging in informal volunteering outside of organizational auspices. This dissertation inspires recommendations for researchers, policymakers, and practitioners working towards improved immigrant integration. Voluntary venues are unique for positive relational dynamics between immigrants and nonimmigrants. More research on the integrative qualities of voluntary work may lead to discovering best practices on how immigrants achieve high levels of integration within communities and beyond.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".