Collaborative lifecycle design A viable approach to sustainable rural technology development
Bibliographic record
Abstract
The purpose of this article is to present a lifecycle design methodology for rural technology development that promotes stakeholders' participation throughout the developmental process. The model was based on findings from rural development studies, several years of experience in rural technology development and on lifecycle management principles. This article explained what collaborative lifecycle design for rural technology development is, and how it can be implemented. It also described how stakeholders involved rural technology design and development framework was used in developing low-cost, potable multi-purpose threshers. The performance characteristics of the machines developed through the use of this methodology were within the same functional performance range with imported models. In addition, the use of the methodology resulted in lower production cost, better acceptance and improved maintainability. It also fostered rapport between stakeholders, led to attainment of self-reliance in that technology instead of dependence on imported machinery and improved our post-harvest technology capacity building. Moreover, it led to the development of a technology that is socio-culturally compatible and environmentally friendly.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".