Deforestation, Erosion, and Fire: Degradation Myths in the Environmental History of Madagascar
Bibliographic record
Abstract
Abstract Mention of the island nation of Madagascar conjures up images of exotic nature, rampant deforestation, and destructive erosion. Popular descriptions of the island frequently include phrases such as 'ecological mayhem' or 'barren landscape'. This paper compares this common wisdom and conservation rhetoric about the environmental history of Madagascar with the results of recent research by paleoecologists and others. Deforestation and erosion, while very real trends, are exaggerated due to mistaken ideas about pre-settlement forest extent and the eye-catching red soils and erosion gullies. The role of fire, principal tool of landscape change and pasture maintenance, is unnecessarily demonised. Blame is placed on the Malagasy people and problems of poverty and population growth, ignoring economic interests, historical political contexts, community politics, and the potential of the people to manage their resources positively. Finally, drawing from the recent school of thought that recognises the role of narratives, discourses, and representations in the politics of conservation, this paper concludes by illustrating the political nature of the oft-repeated story of environmental degradation in Madagascar.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".