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Record W4403549682 · doi:10.54097/x3vy9j69

Analysis of the Causes and Countermeasures of the Phenomenon of “Unfinished Buildings”

2024· article· en· W4403549682 on OpenAlexaff
Yujia Wang

Bibliographic record

VenueHighlights in Business Economics and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicConstruction Management and Sustainability
Canadian institutionsHamilton General Hospital
Fundersnot available
KeywordsPhenomenonArchitectural engineeringEngineeringPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Real estate is very important to the Chinese economy and has driven its rapid development in the past few decades. However, China’s real estate market has experienced fluctuations in recent years, causing numerous attentions from all over the word. With the continuous advancement of the real estate market, the problem of unfinished buildings is also a major hidden danger. The appearance of unfinished buildings not only brings great economic losses to the citizens but also brings huge negative effects to society. This study analyzes the causes of the unfinished buildings from various aspects, such as capital chain break, real estate project engineering problems, real estate lack of self-control, and economic disputes, and puts forward a series of preventive measures according to the causes, finally removing this hidden danger at the root. In order to maintain the balance of the real estate market and protect the good image of the city, new suggestions are provided.

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)0.003
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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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