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Record W1939893422 · doi:10.1177/0974930615578499

Housing Shortages in Urban India and Socio-economic Facets

2015· article· en· W1939893422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Infrastructure Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsEconomic shortageCasteEthnic groupCensusSocioeconomicsTribeEconomic growthGeographyPublic housingBusinessEconomicsPopulationSociologyDemographyGovernment (linguistics)Political science

Abstract

fetched live from OpenAlex

This article discusses the urban housing situation in India using the data from Census and NSS Housing Condition Rounds, and it also estimates the urban housing shortages in 2012 by Caste and Ethnic groups, following the methodology of the Technical Group on Urban Housing Shortage, 2012–17 (TG-12). The TG-12 estimated the number of urban households to be 81.35 million and urban housing shortage to be 18.78 million in 2012. Households living in congested conditions were found to be one of the main factors leading to these housing shortages. Households from economically weaker sections and lower income groups accounted majorly for these shortages. Among caste and ethnic groups, housing shortages were found to be high for Scheduled Caste households than Scheduled Tribe and other households, mainly on account of congestion factor. The results suggest the need for attention towards urban housing with targeted group-specific policies (economic and social) and socio-spatial perspective to eradicate shelter deprivation and to enhance the quality of life of the people in urban India.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.187
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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