Transnational migrant labor, split labor markets, and workers’ boundary-making practices in a Chinese state-sponsored workplace in Ecuador
Bibliographic record
Abstract
Existing research on transnationalization of labor documents split labor markets between less-skilled (im)migrant workers and native workers in the host countries. But there is little research on how labor relations take shape when relatively skilled workers migrate from more developed countries to work temporarily in less-developed countries in the Global South. Based on ethnographic research on a Chinese state-sponsored construction project in Ecuador, this article explicates an understudied case. Although the temporary migrant Chinese workers come from a more developed country and hold higher status jobs, they are compensated at a lower rate and have fewer labor rights compared to their lower status Ecuadorian counterparts. By comparatively examining workers’ everyday interactions and boundary-making practices, this study develops a twofold argument. First, the development strategies and political interests of the home and host states interact to shape divergent recruitment processes and labor policies, which gives rise to disparate working conditions and labor rights between migrant and native workers. Second, the foreign migrant and native workers cope with labor disparities by invoking national stereotypes to draw social boundaries, which exacerbates their labor market splits. This analysis has theoretical implications for understanding labor relations under transnational state capitalism, workers’ strategies against labor control, and the future of labor solidarity in the Global South.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".