Zahra or Zoe, Arjun or Andrew? Bicultural baby names reflect identity and pragmatic concerns.
Bibliographic record
Abstract
OBJECTIVES: Ethnic first names are a visible product of diversity in the West, yet little is known about the psychological factors that influence naming preferences and choices among bicultural individuals. METHOD: = 211) were parents of an Indian background living in three English speaking countries (Canada, United States, UK). They completed an online survey with measures of naming (consequences of ethnic naming, names as markers of cultural identity, actual naming choices) and psychological factors: heritage and mainstream cultural identifications, ethno-cultural continuity. RESULTS: Across all 3 studies we observed a strong preference for ethnic over mainstream names. In Studies 1a and 1b heritage acculturation and motivation for ethno-cultural continuity predicted stronger preference for ethnic names. In contrast, a preference for mainstream names was predicted by mainstream acculturation and expectations of negative consequences of ethnic names. In Study 2 choice of an ethnic name was positively related to beliefs about names as markers of ethnic identity, and negatively related to expectations of negative consequences of ethnic names. CONCLUSIONS: Baby naming among ethnic minorities is a complex cultural decision, reflecting both identity and pragmatic concerns. Implications for studies of acculturation and identity, and future research directions are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".