Towards a Theoretical Construct for Modelling Smallholders’ Forestland-Use Decisions: What Can We Learn from Agriculture and Forest Economics?
Bibliographic record
Abstract
Academic research on smallholders’ forestland-use decisions is regularly addressed in different streams of literature using different theoretical constructs that are independently incomplete. In this article, we propose a theoretical construct for modelling smallholders’ forestland-use decisions intended to serve in the guidance and operationalization of future models for quantitative analysis. Our construct is inspired by the sub-disciplines of forestry and agricultural economics with a crosscutting theme of how transaction costs drive separability between consumption and production decisions. Our results help explain why exogenous variables proposed in the existing literature are insufficient at explaining smallholders’ forestland-use decisions, and provide theoretical context for endogenizing characteristics of the household, farm and landscape. Smallholders’ forestland-use decisions are best understood in an agricultural context of competing uses for household assets and interdependent consumption and production decisions. Forest production strategies range from natural regeneration to intensive management of the forest resource to co-jointly produce market and non-market values. Due to transaction costs, decision prices are best represented by their shadow as opposed to market prices. Shadow prices are shaped by endogenous smallholder-specific preferences for leisure, non-market values, time, risk, and uncertainty. Our proposed construct is intended to provide a theoretical basis to assist modellers in the selection of variables for quantitative analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".