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Record W2073252666 · doi:10.1680/stbu.2009.162.3.161

Britain's tall building boom: now bust?

2009· article· en· W2073252666 on OpenAlexaboutno aff
I. R. Skelton, Peter Demian, Dino Bouchlaghem

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBoomBustQuarter (Canadian coin)RecessionIndex (typography)EconomicsEconomyEngineeringGeographyArchaeologyMacroeconomics

Abstract

fetched live from OpenAlex

From early 2005 up to the freeze induced by the world's faltering financial markets during the first quarter of 2008, Britain experienced a demand for tall buildings of an unprecedented high level: in London alone, ten tall buildings have started, or were due to start on site, between the first quarter of 2007 to the fourth quarter of 2008. This is directly comparable in size with America's Manhattan Island skyscraper boom of the 1920s. The objectives of this paper are: first, to investigate the evolution of the UK tall building and determine the reasons behind this building form's growth at unprecedented rates; second, to define the UK tall building and compare it with the international tall building stage; third, to analyse the differing types of demand and categorise these subsectors of the UK tall building market; fourth, to calculate the size and value of this specialist construction market in the UK and forecast its growth potential; and finally, to analyse the latest negative market developments during 2008 and warn of the current match of the UK tall building market to the Skyscraper Index model and the resulting risk of full-blown economic recession.

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)0.001
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 score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Structures and BuildingsSame topicHousing Market and EconomicsFrench-language works237,207