Postponing Labor in Fisheries, Tourism and Agriculture Sectors: Rural Eastern Indonesian University Students in Java
Bibliographic record
Abstract
This paper explores the migration of Eastern Indonesian university students who come to Java for education. Often from rural, economically disadvantaged regions such as the Kei Islands in Southeast Maluku, and Nusa Tenggara Timur (NTT), these young adults delay joining fisheries, agriculture, or tourism sectors. Instead, these relatively high-performing students travel to the "center of the country" seeking skills and experiences promised by higher education in Javanese urban centers. This qualitative, anthropological research complements other, more technical and economic approaches. Based on interview and observational data, a complicated portrait emerges of these bright young people from fishing and farming communities in Maluku and NTT. Many idealistically plan to return to their home communities, hoping to improve local fishing and farming methods or to work as teachers, civil servants, or tour guides. Others do not intend to return home, where they think jobs are scarce and traditional livelihoods unattractive. Analysis of this generation's perspective has critical implications for educators and policymakers wishing to prevent a "brain drain" of their educated native sons and daughters, whose experience and skills could contribute importantly to the various socio-economic demands present in island regions, including fisheries and agriculture, conservation, tourism, and employment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".