Bibliographic record
Abstract
Training has been a vital component for the growth of retail sector employees.India's retail sector appears backward not only by the standards of industrialized countries but also in comparison with several other emerging markets in Asia and elsewhere in terms of service given by the sales Team. The Indian retail market is estimated to be US$ 450 billion and one of the top five retail markets in the world by economic value. India is one of the fastest growing retail market in the world, with 1.2 billion people. Retail market is growing, not only in terms of numbers but also in terms of stature, image and class. Today customers are changing and their expectations are rising, they are demanding world class service. To meet such challenging demands the sales force has to be well equipped with good soft skills, updated with the latest technology (bearing in mind the necessity of keeping the human element in place because technology in itself, is cold, impersonal and not at all customer friendly) will ensure that brand marketers are able to keep pace in a dynamic world. Increased competition, e-commerce, and mobile commerce, needs innovative skills and developed soft skills of the sales team to help the retail stores aim their high sales targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".