The Determination of a GSM Operator Preference with a Multiple Logistic Regression Analysis: A Youth-Oriented Study at a University in Turkey
Bibliographic record
Abstract
The magnitude of technological developments can be better understood nowadays in line with the developments particularly in the communication and mobile phone industries. These developments do not only trigger the production of new technology for mobile phones but they also bring forward the issue of conveying this technology to users. The applications and services produced for mobile phones are generally introduced to the market via Global System for Mobile Communications (GSM) operators providing communication services. Users’ benefiting from these applications and services in their mobile phones or from their operators, depends on their preference of a GSM operator that has the capacity to meet the users’ needs. This study has aimed to determine the factors that influence the preference of a mobile phone operator among university students, the most active age group using mobile phones. Both factor analysis and multiple logistic regression analysis are used to determine the influential factors and the degree of influence in the preference of a GSM operator. It has been identified that the most influential variable on the preference of a GSM operator is the communication expenses as a socio-economic factor, whereas the brand factor is the most important variable in deciding which GSM operator service to purchase.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".