Bibliographic record
Abstract
This paper analyzes recent policy reforms made to foreign care work in Japan. The two policy reforms discussed in this paper are 1. The expansion of categories in the Technical Intern Training Program (TITP) and 2. The inclusion of domestic workers into the Japanese labour sector through the use of National Strategic Special Zones. By analyzing these policymaking processes, the following four observations were made salient. 1. That policy reforms were largely driven by economic motivations; 2. That the policymaking processes that determined the nature of these reforms were led by politicians who were acting on behalf of the interests of business leaders; 3. That the Japanese government continues to utilize policies that deny labourers permanent residency or citizenship status, such as temporary worker programs, in order to avoid implementing migration practices that allow workers to become Japanese citizens; and 4. That the government holds contradicting attitudes towards care work, whereby eldercare is increasingly considered professional/skilled work, while domestic work is regarded as low/semi-skilled labour. These findings suggest that Japan’s foreign care immigration policies are designed to recruit temporary workers in ways that violate their human rights for the purpose of exploitation, in addition to the original goal of transferring skills to sending countries. With this in mind, I conclude my paper by arguing that these policymaking processes reproduce a gendered, racialized, and classed international division of labour and a global care chain
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".