Elements of indigenous socio-ecological knowledge show resilience despite ecosystem changes in the forest-grassland mosaics of the Nilgiri Hills, India
Bibliographic record
Abstract
Abstract The Nilgiri Hills in the Western Ghats of India constitute a region of high biological and cultural diversity, and include an endangered shola forest-grassland mosaic ecosystem. A mosaic ecosystem is one consisting of adjacent, coexisting patches of highly distinct naturally occurring land states (in this case, shola forest and natural grassland). Changes in the landscape since the nineteenth century have severely impacted the shola-grassland mosaic and challenged the traditional lifestyles of the indigenous Toda people. However, the responses of traditional Toda socio-ecological perspectives and landscape management to these changes have not been explored through population surveys. Here, using a survey method, the article explores traditional Toda perspectives of ecosystem value and landmanagement practices. The survey consists of interviews of 50 respondents belonging to 24munds(villages), covering ten clans, neighbouring mosaic lands, plantations and agricultural areas. The findings show that traditional socio-ecological landscape management is robust and has persisted despite marked ecological and socio-economic changes during the nineteenth and twenty-first centuries, and despite frequent gathering of land management advice from non-Toda. Elements of traditional socio-ecological knowledge that have persisted include prevalent collective traditional decision-making and long-held preferences for a landscape composition with a strong mosaic component. The highly robust nature of Toda socio-ecological culture and land management suggests that the Todas have a valuable role to play in supporting the long-term persistence of the shola-grassland mosaic. Increasing their stewardship role would help conserve this endangered and highly biodiverse ecosystem, while at the same time preserving a unique indigenous culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".