Will the Exodus of Young People Bring an End to Swidden Farming as a Major Forest Use in SE Asia?
Bibliographic record
Abstract
Swidden agriculture has been practiced historically by communities in SE Asia, but as the population grows and other land uses expand, the areas available to swidden farmers are decreasing. Government environmental policies discriminate against swidden farming. Opportunities for off-farm employment are increasing, and this is attracting young people to abandon swidden farming. We explored the link between access to land and migration in three forest landscapes in Indonesia, Lao, and Vietnam. We analyzed the impacts of the push factors within the swidden systems and the pull factors from non-agricultural activities on young people’s decisions to migrate or continue in swidden agriculture. We found that stable cash incomes from non-farm jobs were a major driver of young people’s out-migration. Other factors included the desire to have broader experience, better education, as well as peer influences. We also found that land was becoming less accessible to young swidden farmers, but this was not a major reason to migrate as suggested by many studies. Government and private sector investments in plantations, mining, or infrastructure are reducing land availability. Government restrictions on land clearing also reduce areas available for swidden farming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".