Marketing Reform Interventions in the Performance of World Bank Financed Agricultural Programmes in Trans-Nzoia County, Kenya
Bibliographic record
Abstract
The main objective of this study was examine contribution of marketing reform interventions on the performance of agricultural programmes funded by the World Bank in Trans-Nzoia County, Kenya. The study arose out of the need to quantify the worth of reform packages currently implemented in the agriculture sector thorough innovative interventions. The sample size of this study was 268 respondents determined using the simplified Yamane formula of proportions. Pragmatism school of thought was the best suited philosophy to guide this study as it complemented the epistemological, methodological and axiological underpinnings desired for mixed-mode research. Results obtained showed β weight of 0.181 (F- value (0.029); ρ-value= 0.05) implying that marketing reforms contributed positively to the performance of agricultural programmes. Further analysis generated R=0.125, R2= 0.016 and adjusted R2 =0.012 indicating a better fit for the model and that marketing reform contributed to the performance of agricultural programmes by 1.6%. The analysis also generated F- value (0.029); (p<0.05) and the F-calculated (4.796) being significantly larger than the critical value (F=2.454) suggesting up to 95% chance the model’s strength in explaining it is statistically significant. These results support outcomes theory by providing documented analysis and empirical evidence to support the formulation of research-based policies and regulations. Findings from the study will therefore contribute immensely to the growth of project management discipline and agricultural marketing practices in Kenya and globally.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".