Land use intensification : The promise of sustainability and the reality of trade-offs
Bibliographic record
Abstract
Land use intensification is widely considered to be an essential strategy for achieving global goals to eliminate poverty and to avoid damaging losses of ecosystem services. This chapter investigates whether current land use intensification activities are achieving these twin goals. To do so, it reviews a body of academic literature that reports on case studies in which both social and ecological outcomes of intensification are reported. There are two main findings. First, there are relatively few cases in which land use intensification is clearly succeeding in these twinned objectives. There are many more cases in which, for example, short-term income or productivity gains from land use intensification are resulting in long-term diminution of biodiversity and ecosystem services. Studies with longer-term perspectives are already seeing how such trade-offs are leading to negative feedbacks for human wellbeing, especially for marginalised social groups. Secondly, we learn most from those studies that a) go beyond measuring production and income to measure multiple dimensions of wellbeing and ecosystem services, b) monitor dynamics of outcomes across longer time periods and across landscapes and c) disaggregate outcome measures to identify outcomes for different social groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".