Parspective of Development Disparities in Bankura District, West Bengal
Bibliographic record
Abstract
Regional disparity is a worldwide phenomenon which exits even in various developed countries. The co-existence of such condition in the developing and less developed nations or a region within a nation is known as regional disparity or regional imbalance. Regional disparity may be total or partial i.e. it may be intra-state or inter- state, it may be intra-district or even it may be intra Block. The district Bankura, is situated in the western part of West Bengal. At present, it is the fourth larger district of the state in respect to its size. The district has 3596,292 populations (2011) which shares 3.94% of the state total population and is characterized with predominance of rural population. Out of the total population of the district, 86.52% live in rural areas and are dependent mostly on agricultural pursuits. Out of 3828 Mouzas, 569 are considered as a backward. The District holds a distinct type of physical characteristics and is described as connecting link between the plains of Bengal on the east and the Chotanagpur plateau on the west. Geographically, the district has been distinguished in terms of its distinct physiographic units. The Western part differs from the eastern part in terms of physical as well as development perspectives. This paper attempts to explore the correlation between the three physiographic divisions and the spatiality of human development by using geo-spatial technologies.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".