Postscript: Infrastructuralization: Evolving Sociopolitical Dynamics in Labour Migration from Asia
Bibliographic record
Abstract
This article explores the trend of “infrastructuralization” in state-sponsored programs of low- and semi-skilled labour migration from Asia. These programs increasingly focus on facilitating migration rather than generating actual opportunities for mobility and substantive development. While providing training to develop skills targeting specific jobs in specific countries, the programs generally leave complaints about actual working conditions and wages to be managed by the migrants themselves. In this process, labour migration programs are infrastructuralized, meaning that there is an ongoing expansion and intensification of the socio-technical platform that makes mobility possible, as facilitation becomes an end in itself. This trend is tied to changes in the general development paradigm, labour and state-citizen relations across Asia, as well as the increasing importance of brokers in facilitating connection. This article first probes a number of internal dynamics around which infrastructuralization unfolds in practice. We then highlight how commercial intermediaries and public institutions, the two key actors in infrastructuralization, shape migration by producing context-specific migrant subjectivities, making aspirational work a central element of infrastructuralization. In the conclusion, we explore research agendas that can be developed further.
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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 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".