Bibliographic record
Abstract
India has in the last decade become of the fastest growing entrepreneurial landscapes in the world. With a total population of almost 1.2 billion inhabitants, it has developed from a rural economy into a highly competitive market. This study analyses the spatial configuration across the country from a regional perspective, offering an assessment of the spatial autocorrelation of business as to understand the spatial configuration of what I define as a regional-spatial business landscape. In this study, the patterns of distribution of all the registered Indian businesses are assessed counting a total of 6500 registered businesses from 1850 to 2010, which were geocoded and imported into a Geographic Information System environment. A geostatistical analysis is conducted measuring business growth and performance at a national level by means of a Global Moran’s I calculation and followed by assembling a Local Getis-Ord for regional assessment of correlation of road networks. These local spatial statistics reveal clustering of hot spots within threshold distances of road concentrations, suggesting a positive relation between location of businesses and concentration of road networks. The agglomeration of Indian businesses becomes defined by the importance of road infrastructures to allow commutes and interaction of businesses. As a result, it becomes possible to see that India’s business landscape is far from homogenous, and responds well to Weber’s theory of industrial agglomeration, while predicting possible interfirm collaboration. These business hubs in the business landscape are assessed at national level through spatial autocorrelation and then regionally diagnosed by identifying hot spots of business location given business density, and bringing to light the precise location of India’s business hubs from a spatial business landscape perspective at present.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".