A study on telecommunicationn tower sharing among mobile network operators in Zambia
Bibliographic record
Abstract
Mobile Network Operators (MNOs) Airtel, MTN, and Zambia Telecommunication (Zamtel) in Zambia adopted a tower-sharing model practised in developed countries such as the United States, Canada. At network roll-out, the operators retained ownership of the towers and at the same time provided telecommunication services. As the cost of providing network services to the growing active subscribers increased, recognized revenues dropped. To cut down on costs and improve their services, quality, pricing, and incentives offered, operators sold their towers to IHS Towers and began to lease back space on the towers. The capital recovered from the sale was then invested in new technologies to not only improve services but also to retain and attract more customers. Within this research study, the adoption and effects of the models used during the infrastructure sharing was investigated. Quantitative research was conducted on the telecommunication tower industry and mobile network operations. The research found that all network operators share infrastructure passively through IHS Towers and Zamtel, and no active infrastructure is shared. Among these, 75% of the towers are self-supporting used for voice and data. 54% of these towers are being shared while 46% of the towers share only the sites. The tower-sharing business model was mainly adopted to reduce capex. It is likely that potential entrants could adopt this model as they enter the market. The main
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".