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Record W4311377032 · doi:10.1177/00219096221141343

Spatial Analysis of Access to Basic Services in Urban West Bengal: Disparities and Determinants

2022· article· en· W4311377032 on OpenAlexaboutno aff
Soumyadip Chattopadhyay

Bibliographic record

VenueJournal of Asian and African Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSanitationSewerageLatrineGeographySocioeconomicsInvestment (military)Service (business)Quarter (Canadian coin)CensusBusinessEconomic growthEnvironmental planningEnvironmental healthPopulationEconomicsPolitical scienceEnvironmental engineeringPolitics

Abstract

fetched live from OpenAlex

This paper, using data from Census 2011, examines the availability of treated drinking water, latrine facility, drainage facility and electricity between villages, census towns (CTs) and statutory towns (STs) in West Bengal, India. Urban areas of socio-economically developed administrative divisions experienced higher household service coverage. Availability of drinking water and latrine facilities is distinctly better in STs over CTs and CTs over villages. Other services like latrine with sewerage facilities and closed drainage facilities recorded insignificant differences in household coverage between cities and villages. Results of multi-variable regression models reveal that type of economic activities and social characteristics of the urban areas; nature of basic services and their institutional arrangements significantly influence the service provision. So, the paper argues for stepping up investment, institutionalization of an integrated planning paradigm and strengthening urban governments to ensure equitable distribution of basic services.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.325
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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