Middle-range theories of land system change
Bibliographic record
Abstract
© 2018 Elsevier Ltd Changes in land systems generate many sustainability challenges. Identifying more sustainable land-use alternatives requires solid theoretical foundations on the causes of land-use/cover changes. Land system science is a maturing field that has produced a wealth of methodological innovations and empirical observations on land-cover and land-use change, from patterns and processes to causes. We take stock of this knowledge by reviewing and synthesizing the theories that explain the causal mechanisms of land-use change, including systemic linkages between distant land-use changes, with a focus on agriculture and forestry processes. We first review theories explaining changes in land-use extent, such as agricultural expansion, deforestation, frontier development, and land abandonment, and changes in land-use intensity, such as agricultural intensification and disintensification. We then synthesize theories of higher-level land system change processes, focusing on: (i) land-use spillovers, including land sparing and rebound effects with intensification, leakage, indirect land-use change, and land-use displacement, and (ii) land-use transitions, defined as structural non-linear changes in land systems, including forest transitions. Theories focusing on the causes of land system changes span theoretically and epistemologically disparate knowledge domains and build from deductive, abductive, and inductive approaches. A grand, integrated theory of land system change remains elusive. Yet, we show that middle-range theories – defined here as contextual generalizations that describe chains of causal mechanisms explaining a well-bounded range of phenomena, as well as the conditions that trigger, enable, or prevent these causal chains –, provide a path towards generalized knowledge of land systems. This knowledge can support progress towards sustainable social-ecological systems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".