Ethnographic study of the creation and usage of Diasporic Capital for education and identity construction of Indian Diasporic youth in Montreal
Bibliographic record
Abstract
People from India are the second fastest growing ethno-cultural group in Canada. There are more than fifteen thousand people of Indian origin in Montreal alone. I use “diaspora” as a heuristic tool to understand the social formation and cultural patterns of these international migrants. In this research I have focused on the role of the Indian diasporic community in Montreal, specifically on the educational experiences and identity negotiation processes of youth. I have situated this inquiry within the macro terrain of globalisation and transnationalism, while the micro facets focus on social education as manifested in the contemporary society, marked by different foci of influence and similarly diverse modes of resistance. My research is located within post-formalism, and I propose critical transnational ethnography synergized by the different loops of research bricolage to study people in the diaspora. Based on my analysis, I argue that people in the diaspora invest in and create certain social energy which can be comprehended as the diasporic capital. Diasporic capital must be understood as the combination of different social energies, including but not limited to social capital, cultural capital, human capital, and economic capital. It is my contention that the role of the community on the youth depends on the manner and process through which the diasporic capital is invested in, and is used by the parents as well as the youth themselves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".