Walking between two worlds : the bicultural experience of second-generation East Indian Canadian women
Bibliographic record
Abstract
Second-generation East Indian women represent a visible ethnic minority group in need of culturally sensitive research to facilitate an understanding of their integration into Canadian society. There is a scarcity of systematic qualitative inquires into the experience of this contemporary second-generation population within a North American context. Hence, the primary objective of this study is to understand the bicultural experience of a select group of second-generation East Indian women using a focused ethnography as a research tool. The central questions guiding this inquiry are (a) What are the salient aspects in the subjective experience of second-generation East Indian women as they grow up within both an East Indian and Canadian cultural context? (b) What are some of the challenges they face as a result of their biculturalism, and (c) How do they negotiate these challenges? The sample pool consisted of 16 second-generation East Indian women between the ages of 20 and 40 years who were either working or attending university and who were English speaking. Data collection focused on individual and follow-up interviews, each lasting 60 to 90 minutes. A latent content analysis was used to analyze the interview data and focused on looking for general themes, patterns and trends in the data set. Results suggest that the bicultural experience of this population is a complex and multifaceted phenomenon that reflects the intersection of multiple identities including race, ethnicity, gender and cultural values.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".