Analysis of Socio-Economic Well-Being of Population in Khirthar National Park, Sindh: A Geographical Study
Bibliographic record
Abstract
Pakistan’s two-third population lives in rural areas where the dependence on natural resources is foremost. National parks are protected areas where the natural environment is preserved for the future generations. The purpose of this study is to investigate the socio-economic aspects of the people living in such areas. For this a comparative study has been designed by selecting two areas of Kirthar National Park (KNP), Sindh, one within the boundary of park; Core and the other at the transition zone of the park. The data have been collected through extensive field survey and analyzed using correlation technique. The study can be helpful in assessing the interaction that exists between humans and dry natural environment. The results indicate a clear difference in the standard of living of the people living in these two selected areas. Such studies are very important from the point of view of rural development of local communities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".