Environmental change and adaptation in degraded agro‐ecosystems: the case of highland Madagascar
Bibliographic record
Abstract
While the view that the poorer agricultural populations in developing countries will be at the forefront of negative consequences due to environmental change is widely accepted, this perspective must become considerably more nuanced in order to recognise and take advantage of emerging opportunities for realistic adaptation. This paper presents a case from Madagascar that suggests adaptation opportunities involve more than looking for alternatives to what are presently perceived to be negative socio‐ecologic processes. In Madagascar the severe erosion occurring on the deforested central plateau actually appears to create, over time, opportunities for increased food security and environmental management compared with uneroded portions of the same landscape. The paper proposes that while concern and action are needed to attend to the problems that the poor of the developing world will face due to impacts from environmental change, the repercussions of such change on agricultural systems also need to be looked at in ways that involve recognising the local and aggregate potential opportunities that they may present in certain systems, in order to realise the prospects for adaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".