Bibliographic record
Abstract
In this paper I discuss interviews conducted with Indonesian village women, concerning their decisions to sojourn abroad for work. The women detail three factors that they believe compel them to seek work abroad: lack of job opportunities, lack of educational and training opportunities and personal desire to experience life outside the confines of family and village life. They also raise the issue of government biases in educational and vocational planning that negatively affects villagers’ abilities to find employment within Indonesia, and particularly within rural environments.I also interviewed community support workers who mention political patronage as a factor in allocating funds for educational and training projects. Recent studies indicate that the Indonesian government has, for many years, prioritized formal education at the public-school level in urban centers and larger provincial towns, but that poorer rural villages lack access to similar opportunities. Many Indonesian women working in laboring positions abroad emanate from these poorer villages.Participant recommendations include delinking village educational funding from political patronage, and allocating more funds to remote villages, not only in terms of building more primary and secondary schools but also in terms of providing long-term vocational training, particularly for young adults, which, in combination with increased employment opportunities, may decrease the necessity to migrate.Data was collected through interviews and written journals in Indonesia and Hong Kong between 2005 and 2017.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".