Second Generation Immigrants: Technology Use to Reduce Cultural Dissonance
Bibliographic record
Abstract
Worldwide, millions of people permanently resettle in a new country (Díaz Andrade and Doolin, 2019). It is well known that immigrants are the fastest-growing component of the U.S. population. First- or second-generation immigrants (SGI) comprise around a quarter of the population. With such a large number of populations, the past four decades witnessed the beginnings of dramatic changes in people’s identities and the symbols of those identities. Defined as “native-born children of foreign parents or foreign-born children who were brought to the U.S. before adolescence” (Portes and Rumbaut, 2007, p. 985), SGIs face unique challenges (such as feelings of isolation, cultural dissonance, and identity crisis), which are rarely discussed by the public or academia (Diederich et al., 2022). Social media has become critical in peoples’ everyday lives. People use social media to connect with friends and families, find social support, express cultural identities, and access fundamental needs such as a sense of belonging (Díaz Anrade and Doolin, 2019). U.S. Research shows that second-generation immigrants are a demographic particularly susceptible to mental health struggles or traumas caused by feelings of marginalization and cultural dissonance (Diederich et al., 2022). Therefore, we aim to address the following research question: How does social media use reduce the cultural dissonance of second-generation immigrants? To answer the research question, we conduct a qualitative study with second-generation immigrants in the U.S. We are not so much concerned with their access to social media as with understanding how their use of social media causes their cultural dissonance, which is highly associated with their participation in social, cultural, political, and economic life (Petter and Giddens, 2023). In particular, we explain why and how social media use impacts bicultural individuals’ identification with two or more cultures and their ability to fit in. Theoretically, this study contributes to the broad social inclusion research on technology and marginalized communities by revealing how social media use can impact the cultural dissonance of second-generation immigrants. Our results provide insights into social media usage for social outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".