Rangelands and Pastoralism in Globalized Economies: Policy Paralysis and Legal Requisites
Bibliographic record
Abstract
Growing quest for globalization and expanding economies have resulted into fragmentation, enclosure, grabbing, militarization and devastation of rangelands. Grasslands – covering 70% of the global agricultural area – are the basis for livestock production. In most of the countries, governments have little recognition of communal tenures of agro-pastoralists. Consequently, both pastoralists and rangeland ecosystems have suffered a grim fate. On the contrary, the subsistence pastoralism is an established sustainable strategy of livelihood and ecosystem conservation in the rangelands. Unfortunately, some of the most nutritive foods and other sustainable products of nomadic pastoralists have not desirably been priced in modern markets. With the demonstrated cases exhibiting the nomadic pastoralists, such as Hutsul shepherd communities of Ukraine, as most sustainable societies on planet Earth, there is urgent need for reshaping the popular paradigm and State policies on rangeland commons. In isolation of pastoralist people, the rangelands cannot truly be conserved or protected. To begin with, the resilience of pastoralists to the changing environments and their (unique) rangeland management can first be pondered. Accordingly, the policy and legal frameworks of States need to be reoriented and revised. In particular, Eurasian countries should review their laws and policies on rangeland sustainability and pastoral grazing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".