Globalization, Social Justice, and Migration: Indonesian Domestic Migrant Workers in Malaysia
Bibliographic record
Abstract
Economic globalization, or liberalization, has been one of the major priorities of most national and global economic policies over the last two decades. Components of economic globalization/liberalization include trade and financial liberalization, deflationary macroeconomic policies, fiscal restraint, privatization of state-owned enterprises, and labor market liberalization (Razavi, 2008, p. 1). The adoption of market principles into public management and provision of public services has resulted in the elimination of subsidies, increasing poverty, unemployment, and social inequalities. Vulnerable, marginalized, and weaker sections of society, including unskilled and low-skilled workers and the poor, continue to be disadvantaged in this current economic climate that emphasizes capitalism, market economy profit and competition at the expense of social justice and human rights. Economic globalization with its associated liberalization policies has also resulted in an increase in labor mobility across borders, as in the case of capital and technology. Some people, including high-skilled, low-skilled and unskilled workers, migrate to find better wages and more job opportunities. One of these groups of low-skilled and unskilled workers includes domestic migrant workers. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".