Measuring the level of rurality in the Southwestern region of Bangladesh
Bibliographic record
Abstract
Abstract This research intends to measure and characterize the level of rurality in the southwestern region of Bangladesh using an indexing approach from functional perspective. The findings of the study can be conducive for efficient decision making related to rural development policies and planning. Besides, operational delineation of administrative units, such as Unions and Wards, is essential for the formulation and effective implementation of evidence-based development initiatives. The study has utilized the latest available population census data published by Bangladesh Bureau of Statistics (BBS). The results reveal that Satkhira district exhibits a higher degree of rurality (RI = 4.23) compared to Khulna (RI = 3.58) and Bagerhat (RI = 4.14) districts. This variation among the three districts is ascribed to existing rural–urban disparities. The study underscores the primary influence of socio-economic factors on the rural–urban disparities, with a secondary role for demographic and infrastructural aspects. However, education's impact was notably limited in this context. Insights on intra-district disparities infer that Khulna district exhibits significantly greater rural–urban disparities than the other two. The study also highlights the clustering of non-rural areas along major rivers. This research will assist rural planners and policy makers in understanding the specific rural dynamics of the study area, essential for tailoring fit-to-context development strategies. Moreover, the study provides a basis for classifying the region into clusters to streamline development priorities and resource allocation. Furthermore, the article presents a transferable methodology for evaluating rurality and delineating rural regions in different contexts.
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.
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 | 0.001 | 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 | 0.000 | 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".