Utilization of Network Technologies in Market Information Exchange in Kisumu City, Kenya
Bibliographic record
Abstract
In today’s marketplaces, information products are vital tools with which city governments and businesses can optimize their capacities and make cities more livable. Cities should treat their digital infrastructure and the data as a market creation asset that can propel economic development. Despite the opportunities posted by network technologies, optimal utilization remains a challenge for local authorities and businesses in developing countries. This paper examines the utilization of network technologies in market information exchange in Kisumu City of Kenya based on a case study which involved stratified random sampling of 40 staff of Kisumu City Council and systematic sampling of 260 small business enterprises. The study shows that 72% of business respondents and 77% of City Council respondents used some form of network technology, a clear indication that the technologies has been adopted. The major technology trends include use of internet at 54% by the business enterprises and 67 % by the council staff. Technology has contributed positively to both work and business despite the impediments which include financial constraint and lack of awareness. The Council and the businesses have great opportunities for information exchange through technology. It is discovered that network technologies are used more for non market information exchange, an indication why the technology adoption for business has not moved beyond the substitution stage. It is recommended that the Council sensitizes the businesses on network technologies and the potentials therein. The government should explore installation of cheaper and faster internet connectivity as well as enact viable policies to propel market information exchange.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".