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Record W2737294819 · doi:10.5539/ijef.v9n8p221

Efficient Use of Capital: Paradox of Real Estate and Industry in Turkey

2017· article· en· W2737294819 on OpenAlexvenueno aff
Ali Hepşen, Mehmet Aşıcı, Olgun Aydın

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateEconomicsCurrencyCapital (architecture)TurkishEconomyPrivate sectorFixed capitalEstateFinanceMarket economyCapital formationMacroeconomicsFinancial capitalEconomic growthHuman capital

Abstract

fetched live from OpenAlex

In recent years, one of the hottest debates on Turkish economy is the conflict on resource allocation between real estate and industry sectors. The debate was so intense that ex-minister of Economy Mr. Ali Babacan declared his opinions. Mr. Babacan’s statements about the creation of fixed capital by the private sector is not promising, and private sector fixed capital expenditures are not in the desired level. This situation is due to the limited economic growth and future economic growth. In this study, we have investigated whether Mr. Babacan’s statement is right or not. We have discussed the reliability of the measurement of real estate output as Gruneberg and Folwell did in 2013 and Ruddock did in 2002. That could be concluded that we agree with ex-minister of Economy Mr. Babacan’s comments regarding to imbalances among sectors are threatening Turkish economy’s stability. The imbalances are favoring residential real estate investments and consequently the country is exposed to currency risk.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.242
Teacher spread0.203 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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