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University Towns: Emerging Sector in India

2019· article· en· W2979536896 on OpenAlexaff
Choyimanikandiyil Kala, India Fab, Louis Philip, V.W. Lee, Allen Heusen, Pepe Jeans, C. Day, Pizza Hut, Baskin Robbins

Bibliographic record

VenueInternational Journal of Innovative Technology and Exploring Engineering · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsMetropolitan areaCensusGeographyPopulationEconomic growthLocalityPopulation growthRegional scienceSocioeconomicsSociologyDemographyEconomicsArchaeology

Abstract

fetched live from OpenAlex

According to U. S. Department of Commerce, U.S. Census Bureau , University City are those cities or metropolitan statistical area where total population is between 2,50,000 and one million, out of which minimum 10% should be the student population. Meanwhile college towns are where students do not continue to settle in that locality unlike University Cities where students find an employment and continue staying. Articles show that no relevant or successful University cities exist in India so far. Nonetheless there are a lot of Urban Outgrowths like college/university towns which have developed in India over the years that have made an impact in the growth of town locally in various sectors. The author tries to make an attempt to look at the effects of existence of University in the population and the growth of nearby public places through observations of an Indian town incorporating the knowledge of existing research in the field available globally.

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)0.000
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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