Mobile Number Portability: A Case Study of Kenya
Bibliographic record
Abstract
In the telecommunications industry, mobile numbers are increasingly being seen as an asset of the regulator. The freedom of the customer using it is left to him/her to decide which service provider to use while retaining the same number. Mobile number portability (MNP) has been introduced to provide a platform for this freedom to the customer. The Telecommunications market Regulator in Kenya, the Communication Commission of Kenya (CCK), began the course of mobile number portability in 2010 through newspaper advertisement. The regulator had an aim that in the end, the right customer experience will be provided by the service providers, and help service providers to build profitable and lasting relationships between the service providers and their customer, and to differentiate themselves in the market. In this paper, we seek to evaluate the performance of MNP in Kenya since its launch. This paper seeks to find out how the service has performed after the first three months of operation. We survey and analyze MNP framework in Kenya and compare that to MNP in Japan, Finland, Sweden and Hong Kong to establish the future of MNP in Kenya. It first looks at the MNP framework as used in Kenya and the procedure for reversal in case the customer is dissatisfied with a service provider who moves to and makes a reference to how the service has performed in other markets such as Finland, Sweden, and Hong Kong in order to enable comparative observations. Since there has been very little literature published for countries in Africa, it will only make comments on countries like Egypt, South Africa and Nigeria. Further, it gives recommendations to the participating parties.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".