Relational Direction Model (RDM) of E-Retailing Developed during Research on Direction of Online Retailing (In the Context of United Arab Emirates)
Bibliographic record
Abstract
Global revenues in 2013 from online retail sales have crossed the $1Trillion barrier, getting an extra push from the expected jump in Internet user numbers. The primary objective of this research is to generally examine and analyze the scope and future of online retailing in UAE. In order to get more accurate results, the main objective of this research was divided into very specific contexts which all explored the concept of online retailing in UAE at the final point. One particular aspect to examine is to find out the current attitude of the UAE population towards online shopping based on UAE web stores. And further look into what commonly motivates the customers to go online and buy their preferred items instead of going to a physical shop. Final aim of this study is to discover how a typical online retailer performs in UAE, in terms of security, costs, and product offerings.This study is an exploratory in nature, and both primary and secondary data is deployed. Secondary data is collected from one hundred thirty academic journals (e-journals) from different parts of the world and text books studied from literature review perspective; The Primary data is collected by executing an online survey served through questionnaire (questions resulted from factors and variables collected through studying e-journals).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".