Bibliographic record
Abstract
The Indian retail market, the fifth-largest retail destination globally, has been ranked as the most attractive emerging market for investment in the retail sector. The booming retail sector in India offers newer opportunities to global brands to showcase our products in the right ambience. While Indian retail witnessed a boom some time back, it is now maturing into an expansive platform that will eventually enable tech brands to make their products available in all parts of the country. India’s retail sector, which accounts for about 10 percent of the country’s GDP, remains one of the developing sectors in India. With the expanding middle and upper class consumer base, there will also be opportunities in India's medium and small cities. Greater availability of quality retail space such as department stores, hypermarkets, supermarkets, and speciality stores finding more and more acceptance. India has been ranked as the third most attractive nation for retail investment among 30 emerging markets. Most of the developed economies (US, UK, Canada, Germany, France, Italy and others) around the world have benefited enormously by liberalizing their retail sector. For this to happen, the government will have to bring about the required liberalization in the retail sector, if India is to ever become a developed economy. Historically, the Indian retail sector has been dominated by small independent players such as traditional, small local panwalas (grocery stores) and others. Recently organized, multi-outlet retail concept has gained acceptance and has since then accelerated. Many global players in the organized retail sector are keen to enter the Indian retail market. A large young working population, nuclear families in urban areas, along with increasing working-women population and emerging opportunities in the services sector are going to be the key growth drivers of the organized retail sector in India.
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 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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".