Evaluating Urban Services in Neighbourhoods Through the Inequality Lens in Khulna City, Bangladesh: A Geographic Information System Approach
Bibliographic record
Abstract
This article delineates the development of an integrated assessment tool to understand inequality in the provision of urban services among neighbourhoods in Khulna city, Bangladesh. The study considers eight key indicators to evaluate basic urban services related to the physical environment of the city. We use primary and secondary sources of information as well as geographic information system (GIS) to summarise the results in a scientific fashion. Additionally, we adopt the analytic hierarchy process (AHP) technique to distribute weights among the considered indicators and categorise the neighbourhoods as ‘good’, ‘average’ or ‘poor’ in terms of service availability. Results from the study demonstrate that a majority of the neighbourhoods lack basic urban services. Only 10 per cent of the neighbourhoods are equipped with basic urban services, whereas 74 per cent face difficulties with ‘average’ quality of services and nearly 16 per cent of neighbourhoods fail to provide basic services to city dwellers. Our study may be useful to development authorities, city corporations and local governments to visualise the neighbourhoods struggling for basic urban services and bring them under immediate attention to deliver the required resources. Further, the study provides an assessment model to understand urban service inequality in cities with similar characteristics in other parts of the world.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".