A Cultural Dimensions Model based on Smart Phone Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the major factors influencing the phenomenal growth of the smart phone market is the active development applications based on open environments. Despite difficulties in finding and downloading applications due to the small screens and inconvenient interfaces of smart phones, users download applications nearly every day. Such user behavior patterns indicate the significance of smart phone applications. So far, studies on applications have focused mainly on technical approaches, including recommendation systems. Meanwhile, the issue of culture, as an aspect of user characteristics regarding smart phone use, remains largely unexamined throughout the world. Hence, the present study attempts to analyze the highest ranked smart phone applications downloaded and paid for that are ranked the highest in 10 countries (Korea, Japan, China, India, the UK, USA, Indonesia, Canada, France, and Mexico) and we then derive the CDSC (Cultural Dimensions Score of Content) for these applications. The results derived are, then, mapped to the cultural dimensions model to determine the CISC (Cultural Index Score for Country). Further, culturally significant differences in smart phone environments are identified using MDS analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it