How Does the Indian Diaspora Help Drive Trade and Investment Ties between India and North America? An Exploratory Study
Bibliographic record
Abstract
This study examines the role that the Indian diaspora plays in helping to drive trade and investment ties between India on one hand and Canada and the United States on the other. The Indian diaspora is becoming increasingly important in both political and economic terms in North America. As trade and investment ties continue to grow between a fast developing India and Canada and the United States, the Indian diaspora has been playing an important role in driving this relationship. This study utilizes the concepts of acculturation, bicultural identity, brain circulation, social capital literature and investment theories to analyze the impact that the diaspora has on this relationship. It examines the complex attitudes that the diaspora has towards the home and host countries, and looks into how these help to drive their actions towards these countries. It points out the differences between the attitudes and activities of the Indian diaspora in the U.S. and the attitudes and activities of the Indian diaspora in Canada. It also tries to determine whether the current theories of investment do in fact predict the behaviour of the Indian diaspora when it comes to their investment and trade facilitation behaviours. This is a two-part study that employs both qualitative and quantitative methods. The first part of the study involves a questionnaire survey with 158 managers, executives and entrepreneurs of Indian descent living in the U.S. and Canada while the second part involves more detailed follow up interviews with 25 of these respondents. The results indicate that the Indian diaspora does play an important part in driving trade and investment between Indian and North America. However, there are clear differences between how the diaspora in Canada and how the diaspora in the U.S. does this.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".