Are Poor Really Poor in Pune City
Bibliographic record
Abstract
The India’s economy is experienced boost of 8.6% growth rate by industrial production and services in the first quarter of the year 2010. The cities which are contributing to this growth rate majorly are Delhi, Mumbai, Chennai, Bangaluru, Hyderabad and Pune. This paper concentrates on, the effects of the above said growth on the poor of the Pune city. The city is not only developing because of the boom in IT sector but also growth in agro-business and manufacturing industry. This has not only increased per capita income which is currently Rs. 46,000/-, the highest in country but also has helped raise the living standards of the unskilled labour force. To show this effect a primary survey was conducted which reflects that the living standard of the labour class has gone up, which gives a clear indication for the rise in migrant population in the city. The study reflects that the migrant population is hand in hand in contributing to the tax generation by the Municipal, which in turn is planning to invest INR 1,960 million by 2011-12 on land use and development planning purpose. This will generate huge employment opportunities for unskilled labour residing in the cities slums.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".