Immigration, trade and ‘ethnic surplus value’: a critique of Indo–Canadian transnational networks
Bibliographic record
Abstract
Abstract It is often argued that countries hosting large populations of skilled immigrants might benefit from their cultural and economic competencies in the development of international trade networks. Yet, in so doing, the state can be criticized for fetishizing the ethnic immigrant in market terms in order to extract ‘ethnic surplus value’. In this article, I examine these debates empirically in the case of India–Canada immigration and trade using interviews with traders, officials and immigrant entrepreneurs in British Columbia, Canada. Findings suggest that the supposedly positive relationship between trade and immigration is not obvious in the India–Canada case and there is no convincing evidence of the state managing successfully to extract ‘ethnic surplus value’. Rather, what appears most compelling is evidence of what can be termed a discourse of regional disadvantage circulated by immigrant and non‐immigrant business actors alike regarding the nature of India–Canada relations. Interview respondents link this discourse of disadvantage to the regional history of Indian immigration to Canada, which has traditionally comprised Sikhs from rural Punjab, and it functions to essentialize Indian immigrant ethnicity spatially within both the Indian and Canadian contexts. I focus on the theme of the extraction of ‘ethnic surplus value’ and regional disadvantage to reveal the limitations of both arguments about the economic nature of immigrant‐led network development. In both cases, I challenge these ideas with a critical emphasis on the role of immigrant agency and offer a more nuanced and complicated reading of the role of the state. As a result, I offer a detailed reading of how socio‐spatial immigrant networks are formed and operate at the regional scale, and how this complicates more abstract theoretical formulations regarding the trade and immigration nexus.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.035 | 0.058 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".