Sustainable livelihoods in Kalahari environments a contribution to global debates
Bibliographic record
Abstract
This edited collection provides a comprehensive overview of the dynamics of contemporary natural resource based livelihoods and implications for their sustainability in the context of the Kalahari environment of southern Africa, a region subject to marked spatial and temporal natural variability. Each chapter is written by an active Kalahari researcher and addresses, from an environmental or a social perspective, the implications of different policies for rural livelihoods and coping strategies. In each chapter one or more of the key tenets of environment, policy and structural land use change provides the central element around which the sustainable livelihoods theme is considered. Although the focus of the book is the Kalahari, introductory and concluding chapters, in turn, contextualise the research and discuss key enviro-development issues which resonate across the individual chapters with relevance for wider global debates. Contributors to this volume - David S.G. Thomas, University of Sheffield Deborah Sporton, University of Sheffield Chasca Twyman, University of Sheffield Jeremy S. Perkins, University of Botswana G. Stuart-Hill, World Wide Fund, Namibia B. Kgabung, University of Botswana Andrew Dougill, University of Leeds Robert K. Hitchcock, University of Nebraska - Lincoln Jacqueline S. Solway, Trent University, Peterborough, Ontario
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".