Vishal Mega-Mart- An Overview
Bibliographic record
Abstract
In the background of high consumerism and income of the urban consumers, in recent year, a number of companies have expressed their interest towards retail sector outlets. As a result, numbers of shopping malls have started their operations in metro and urban areas. Pantaloon, big bazaar, Vishal Mega Mart, Reliance Fresh are the best known examples of retail sector outlets in India. Retailing is the interface between the producer and the individual consumer buying for personal consumption. This excludes direct interface between the manufacturer and institutional buyers such as the government and other bulk customers. A retailer is one who stocks the producer’s goods and is involved in the act of selling it to the individual consumer, at a margin of profit. As such, retailing is the last link that connects the individual consumer with the manufacturing and distribution chain. Some of the key features of retailing include: -Selling directly to customers without having any intermediaries -Selling in smaller units / quantities, breaking the bulk -Present in neighborhood or in the location which is quite convenient to the customers. -Very high in numbers -Recognized by their service levels -Fitting any size and or location The objective of this article is to study the Marketing Mix and Shareholding pattern of Vishal Mega-Mart, a renowned name in Retail Industry of India.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".