The Environmental Effects of Forced Displacement in Burma's Karenni State
Bibliographic record
Abstract
Displaced Karenni people on the Thai-Burma boarder were interviewed to investigate the environmental effects of conflict-based displacement on the Karenni's traditional knowledge and livelihoods strategies. As a framework for analysis the reasons for displacement were categorized. Living situations were categorized in terms of the characteristics of a location, and in terms of the sequential stage of displacement (ie. point of origin, second location, third location and refugee camp). Results were mixed. It was found that refugee camps, second and third locations had fewer natural resources which Karenni people depend upon, higher levels of deforestation, lower levels of food security, higher levels of human rights violations leading to poverty, fewer policies and systems in place to protect the environment, and, in some cases, shorter agricultural fallow periods. Refugee camps faced unique environmental problems especially with waste management. There was not found to be an increase in environmental knowledge exchange with migration. It was also found that many areas which had been abandoned due to displacement had their environments degraded and natural resources exploited by government or private companies carrying out macro-development project or logging operations. The high density of populations in the refugee camp, second and third locations led to an exhaustion of natural resources and agricultural land. Poverty was exacerbated by displacement. It led to a dependance on unsustainable income generating activities, and undermined the Karenni people's ability to adapt to new environmental challenges. Displacement also removed traditional environmental protection mechanisms. On the other hand, Karenni is a region of high conservation importance, and the displacement of people from large regions may be preserving ecosystems void of human activity, but this is unproven and warrants further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".