Significance of social networks in sustainable land management in central Ethiopia and eastern Uganda.
Bibliographic record
Abstract
Social networks (SNs) are social frameworks that form good entry points for business and socio-economic developments. Social networks are important for small-scale, resource-poor farmers in Sub-Saharan Africa, who overly rely on informal sources of information. SNs provide opportunities for establishing effective functional multi-stakeholder Innovation Platforms (IPs) necessary for catalysing wide adoption of SLM innovations. This paper analyses the significance of SNs in sustainable land management (SLM), focusing on stakeholders’ characteristics and their association among agricultural rural communities in central Ethiopia and eastern Uganda. The analysis conducted in both countries revealed a positive relationship between the strength of social networks and SLM innovation practices. The closeness of centralisation of networks in Ethiopian and Uganda was 56 and 45%, respectively; implying that only about half of the potential networks among partners actually function. The factors associated with strength of the networks included the age, the physical distance between partners, frequency of interaction, and source of information. Unfortunately, significant weaknesses remain in the existing networks. There exist several networks in both countries without active interactions with key actors in land management. Also, private sector networks particularly important in playing critical roles such as fostering market-led innovations for enhanced adoption, are conspicuously lacking.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".