MétaCan
Menu
Back to cohort
Record W2593896457

Housing and construction chapter 11

2015· article· en· W2593896457 on OpenAlexaboutno aff
Delton Alderman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEurosUnderemploymentDebtValue (mathematics)EconomicsBusinessEconomyFinanceUnemploymentEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Highlights •The new residential and renovation construction markets in the US and the Eurozone were valued at $338.7 billion (305.5 billion euros) and $673.3 billion dollars (614.4 billion euros), respectively, in 2014. •In Europe, 55% of the value of the new residential and renovation construction markets in 2014 was in renovation; in the US, this figure was 30%. •The housing construction market in Europe is still subdued, in part due to the effects of the global financial crisis and the tepid nature of European economies. Nevertheless, residential housing construction is projected to improve by 2.4% in 2015 and by 4.3% in 2017. •Housing completions achieved record levels in the Russian Federation in 2014, with nearly 1.1 million new dwellings put in place, an increase of 20.3% from 2013. •The US housing market continues to stabilize and improve in all its sectors, but it is still hindered by slow economic growth, slow household formation, student debt, underemployment, declining real median incomes, and a constrained housing inventory. •High-value houses and the multi-family market exhibited above-average construction and sales in the US in 2014, but single-family construction remains substantially below its historical average. •Canada’s economic fundamentals improved in late 2014; they are projected to continue to improve in 2015 but decline slightly (from 2015 levels) in 2016. Forecasts suggest stable housing demand and starts in 2015 and 2016.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.160

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicUnderground infrastructure and sustainabilityFrench-language works237,207