The Interplay between Informal and Formal Bylaws in Supporting Sustainable Crop Intensification in the Uganda Potato Production System
Bibliographic record
Abstract
The study assessed the interplay between informal and formal bylaws in supporting sustainable crop intensification, using a case of potato crop production in southwestern Uganda. The study used a descriptive case study design to understand and accurately describe the experiences of farmers in the potato crop subsector in the region. This involved mixed study approaches that ensured coded meaning of consistent responses to the study, and descriptive statistics facilitated sequential understanding of findings and how each related to one another in respective themes. The numerical scores enriched the findings by authenticating the qualitative outcomes of the study to minimize bias. The study used review of documents and literature; six Focus Group Discussions; and 22 Key Informant Interviews to gather diverse experiences of respondents patterns of responses, the main factors or categories, and key responses under every category. The Study found that the greatest informal bylaw was eucalyptus growing (50 percent), followed by permission to graze (18 percent), and control damping (18 percent). The widely represented formal bylaws had a comparatively lesser role in supporting SCI, although with greater emphasis on quality seed (22 percent). Formal bylaws were stronger at setting clear boundaries between users and resources (18 percent), users having procedures for making own rules (11 percent), regular monitoring of resources and users (15 percent), issue sanctions (16 percent), conflict resolution (15 percent), and coordinated activities (3 percent) than informal bylaws. The major benefits for operating as institution were the collective strategy for the market (26 percent), which was less to guarantee sustainable livelihoods for farmers. Individual farmers were driven by desire for faster benefits (13) and preferred following own rules (12 percent). There was more emphasis on market access, regardless of the nature of produce output (35 percent), whether the market worthy or not, and less on environment sustainability. The informal and formal bylaws are separate but united for a common purpose of intensifying potato crop production. Nonetheless, even when combined, they are not strong enough to support SCI. There is a need to strength bylaws on soil and water conservation, improved and quality seed potato and environment sustainability to support SCI, which provide the basis of greater markets and sustainable livelihoods.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".