Managing Linkages between (Communal) Rangelands and (Private) Cropland in the Highlands of Eastern Africa
Bibliographic record
Abstract
"Throughout eastern Africa the economic base of tribal societies has gone through rapid change, with concomitant declines in common pool resources and emergence of new tenure systems (public and private, informal and state regulated). The rapid pace of cultural, political- economic and environmental change have put significant strains on traditional management systems and coping strategies mechanisms that once provided for basic needs of residents while to a large degree maintaining ecosystem function. Compounding the challenge is a rapid erosion of self- reliance among local communities with the influx of external knowledge and economic systems, and of modern institutional reforms (administrative, religious, educational). \n \n "These historical dynamics have created a number of problems for rangeland resource management, including decreased productivity, degradation of water resources, and increased conflict and competition. However, addressing rangeland resource issues requires a holistic understanding of livelihood systems, including trade-offs and interactions between communal rangelands on the one hand, and private property and cropland on the other. Communal rangelands in eastern Africa are interspersed with individual cropland both spatially and temporally, creating strong functional linkages that define both the problems affecting rangelands and the potential solutions. Viewing rangeland resource problems in relation to other resources demonstrates the need for integrative solutions, including an explicit recognition of the linkages between common and private property, user groups (in terms of social trade-offs) and disciplines (technological, social, policy). \n \n "This paper presents data from two benchmark sites of the African Highlands Initiative (AHI) in the highlands of central Ethiopia and northeast Tanzania, respectively. Results of individual interviews and ethnohistorical research with elders, conducted as part of a preliminary watershed exploration exercise in these sites, are presented. They paint a picture of current land use systems, how these systems evolved over time and key 'forcing functions' behind these changes. Both watershed-level diagnostic activities and historical trends analyses point to disturbing trends in natural resource degradation over time, how such trends have impacted upon rangeland resources and livelihoods, and the nature of interventions required to ameliorate both trends and outcomes."
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".