Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
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.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it