Coastal Livelihood Transitions: Socio‐Economic Consequences of Changing Mangrove Forest Management and Land Allocation in a Commune of Central Vietnam
Bibliographic record
Abstract
Abstract This paper reviews the evolution of land use and mangrove forest management in a coastal commune in Central Vietnam from its early period of environmentally sound management under a common property regime, through State and cooperative management, to individual household allocation under the economic reforms of the 1990s. It analyses in particular the introduction of shrimp culture and its environmental and socioeconomic consequences. The case study demonstrates that, while opening up many economic opportunities, Vietnam's economic reforms have had uneven impacts on income inequality. Like many cases in Asia and Latin America, the disruption of common property resources – through the introduction of aquaculture as a livelihood opportunity and producer of an export crop – leaves farmers indebted and natural resources polluted. But, ironically, it was the financially better‐off aquaculture farmers, who had more capacity for risk‐taking and investing, who ended up most indebted, in comparison with poorer farmers who had already sold their ponds. The latter were less integrated into the market economy and relied more on marine product collection. This paper suggests that attention to local contexts and histories can contribute to a better understanding of the causes and consequences of environment‐poverty interfaces.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".