Bibliographic record
Abstract
In the Amazonian agricultural frontier, pasture for cattle is an important and potentially damaging form of land use due to erosion as pastures degrade. This dissertation presents three approaches to understanding policy options to govern this land-use problem: 1) a systems dynamics model (SDM), 2) empirical social research, and 3) an agent-based model (ABM). In the SDM, I examine the role that river basin councils (RBCs) – one of the water governance options in Brazil’s National Water Act – might play in managing this non-point-source pollution issue in the Amazônian State of Rondônia. I compare the central tool of the RBC, a bulk water charge (BWC), to a stylized land-use fine (LUF) for failing to maintain riparian cover, across several scenarios of climate change. The results show no significant advantage to the BWC over LUF in reducing erosion while keeping ranching profitable; moreover, the comparative success of programs similar to LUF suggests these programs may have potential to manage agricultural pollution in the region. One program in Rondônia is the environmental licensing program for rural properties (LAPRO), which requires farms to remove significant amounts of land from production, and may shift production intensity as farms comply. I present empirical data from Rondônia’s Ji-Paraná River Basin that show decreased production intensity and income diversification on larger properties. These results suggest that for smaller properties, complying with LAPRO may bring an increase in land sale to cover debts and an increase in land consolidation in the region. Examining this further, I develop an ABM of ranching and land exchange, inform it with results from my survey research, and investigate the outcomes that could be expected from LAPRO in the context of climate change. Model results show that while LAPRO may increase forest cover in ranching landscapes, it may occur at the expense of the small producer. To the extent that effective monitoring and enforcement exist, a focus on larger holdings will help to mediate this negative social impact. These results suggest that a middle ground may exist in cases where current environmental goals conflict with legacies of past colonization and resource-use regimes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".