Capturing the multidimensionality of land-use agents in a deforestation hotspot
Bibliographic record
Abstract
Given increasing recognition that strategies to transition to sustainable land use should be context specific, structuring the diversity of land-use agents is important. This is particularly so for the world’s tropical deforestation frontiers, where rapid land-use change, driven by diverse agents, leads to stark social-ecological trade-offs. Focusing on the Argentinean Dry Chaco, a global deforestation hotspot, we employed archetyping to identify key types of land-use agents using data from a questionnaire survey covering three main dimensions: agents’ capital assets (what they have), agents’ activities and management (what they do), and agents’ personal characteristics (who they are). We identified five well-differentiated types of land-use agents: forest-dependent smallholders, semi-subsistence ranchers, crop–livestock farmers, agribusiness farmers, and commercial ranchers. Characterizing these major agent types yielded three main conceptual and methodological insights. First, we reveal considerable heterogeneity of land-use agents in the Argentine Dry Chaco, allowing us to move beyond the common yet oversimplified and dichotomic view of agribusinesses vs. smallholders. Second, the agent typology based on all three dimensions captured the diversity of agents much better than any one-dimensional typology alone, demonstrating the value of richer descriptions of land-use agents. Third, all our agent types share characteristics in some dimensions yet differ in others (e.g., forest-dependent smallholders and crop–livestock farmers were similar in who they are, yet different in what they do), explaining how more simplistic agent descriptions arrive at oversimplified agent types. Overall, our work highlights how archetyping can structure complex human–environment phenomena, diverse land-use agents in our case, for guiding tailored, actor-specific policy interventions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".