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Record W2118947320

광역시 주택가격 변화의 특징과 요인 분석

2008· article· ko· W2118947320 on OpenAlex
한동근

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

Venue국토연구 · 2008
Typearticle
Languageko
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaHouse priceFalling (accident)EconomicsDemographic economicsPrice indexPanel dataPopulationQuarter (Canadian coin)GeographyEconometricsDemography
DOInot available

Abstract

fetched live from OpenAlex

This article explores the pattern of house price movements in Korean metropolitan cities during the period of mid 1980s to 2000s. The pattern is characterized by big discrepancy in increasing rates of house prices across big cities in 1980s, falling house prices at similar rates in 1990s, and heterogeneous changing rates of house prices with some cities experiencing soaring price while other cities experiencing falling price in 2000s. Regression analysis using panel data shows that real GRDP has the strongest impact on house price in respective cities, along with real GDP and real interest rates, Net inflow of population is estimated to have a positive impact whereas dishonored bill ratio has a negative impact. The result also indicates that region specific factors outweigh nation-wide macro factors in determining the regional house prices, and that trend becomes even stronger over time. This offers an explanation on the pattern of house price movements in metropolitan cities. Moreover, our study suggests that we need to introduce a region-specific housing policy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.012

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 head0.210
Teacher spread0.187 · 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