“Stuff that only mixed‐race people would understand”: Community and identity‐related experiences in online groups for multiracial people
Bibliographic record
Abstract
Abstract Multiracial people are a rapidly growing group who may still lack social support from similar others in their offline lives. This study aimed to understand the user experiences in online groups for multiracial individuals. In a cross‐sectional online survey, 300 multiracial emerging adults (79% women; mean age = 23; most common ethnic identity components: 86% White/European, 44% Chinese, 17% Japanese, 16% Southeast Asian, and 13% Filipino) answered open‐ended questions about their experiences in Facebook groups for multiracial people. Participants described aspects of the groups they liked and disliked most, as well as their identity‐related experiences in the groups. We used reflexive thematic analysis to generate four themes within participants responses: (1) online groups as a gateway into connections with other multiracial individuals, (2) deriving a sense of belonging and community, (3) an opportunity for exploration and validation of racial identity, and (4) negative intrusions such as negative content (e.g., trolling, repetitive posting) and racism. For racial and ethnic minorities such as multiracial individuals, Facebook groups provide a space for community‐building and identity‐related experiences. Although online groups can foster a strong sense of community, validation, and belonging, findings also highlight potential challenges and limitations (e.g., how to delineate group boundaries, difficulty forming close relationships). Online group users and researchers should explore novel ways of maximizing these positive experiences while addressing users' concerns and negative experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".