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Record W403387035 · doi:10.3130/aija.66.119_1

CONSIDERATION ON TRANSFORMATION OF STREET BLOCKS IN JAIPUR CITY, RAJASTAN, INDIA : An analysis on city maps (1925-28) made by survey of India Part 3

2001· article· en· W403387035 on OpenAlexaboutno aff
Shuji FUNO, Lanshiang HUANG, Shu YAMANE, Naohiko YAMAMOTO, Kikuma WATANABE

Bibliographic record

VenueJournal of Architecture and Planning (Transactions of AIJ) · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsBazaarCityscapeQuarter (Canadian coin)GeographyPopulationHinduismField surveyCasteSocioeconomicsArchitectural engineeringSociologyCartographyArchaeologyPolitical scienceEngineeringDemographyLawVisual arts

Abstract

fetched live from OpenAlex

The objective of this study is to consider the principles of space formation of Jaipur City that is known as so-called a grid (chessboard) city. Jaipur City designed by Jai Singh II (1688-1743) is thought to have been constructed based on the Hindu idea of town, about which many scholars are still discussing. The cityscape has been very uniquely regulated by using the prototype of urban house called haveli (courtyard house) and bazaar building system along the main street. But the population increase is so rapid that living quarter is drastically changing. This paper firstly clarifies the differences between the forms of dwellings we can identify by the City Map (1925-28) made by Survey of India and those we identified based on the field survey in 1996 and discusses the transformation for these about 70 years. The major structure of the city was already constructed in the middle of 18c. Vertical extensions in order to absorb the population are seen all over the city and the society is mobilized especially by the influx of scheduled caste. This paper discusses the trend of transformation of the city block by focusing on the changes of house form.

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 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 score0.197
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.296
Teacher spread0.258 · 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 teacher head, 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

Citations0
Published2001
Admission routes1
Has abstractyes

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