Bibliographic record
Abstract
Although Kenya is the most successful producer and exporter of fresh produce and flowers in sub-Saharan Africa, other countries both in Africa and elsewhere, offer strong competition that could erode export market share in future. Increased labor productivity is crucial for Kenya’s competitiveness. This study aimed at examining the key drivers of labour productivity in flower farms in Naivasha, Kenya. Descriptive survey design was employed and stratified proportionate random sampling technique used to select 381 respondents from who data was collected using a questionnaire. A log-linearized Cobb-Douglas model was used examine determinants of labour productivity. The results showed that workers’ participation in Labor unions, Information & Communication Technology and workers’ skills acquired through training were the major factors that determined labour productivity by 35.4 percent, 19 percent and 14.7 percent respectively. While worker’s wage increase and tools used by a worker influenced labour productivity by 9 percent and 11.4 percent respectively. Worker’s level of education and worker’s experience also increased labour productivity by 5.1 percent and 4 percent respectively. The study recommends that; the Kenyan government should give special attention to education to produce skilled and innovative workers. Flower Farms should invest more in training of workers to acquire relevant skills, acquisition of appropriate tools; improve ICT infrastructure and support labor union in the flower farms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".