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Record W3013316684

ANTI-CRISIS SYSTEM TO FINANCE CONSTRUCTION ENGINEERING, EXEMPLIFIED BY MOSCOW PROGRAM âPEOPLEâS GARAGEâ

2015· article· en· W3013316684 on OpenAlexaboutno aff
E. D. Shcherbachev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Industrial Development
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyFinanceEconomic shortageGovernment (linguistics)CashBusinessFellFinancial crisisQuarter (Canadian coin)Economics
DOInot available

Abstract

fetched live from OpenAlex

The global financial crisis has made the Russian financial system staggering, and the necessity arouse to attract additional funds to various industrial branches. The most acute shortage of resources fell to the building industry. In the fourth quarter of 2008 there appeared a real threat of bankruptcy for the majority of building enterprises because of the absence of new construction orders and curtailed funding the completion of objects under construction. The most difficult was the situation at the enterprises which are not operating under state orders (allocation of budgetary funds for the construction of social objects has not been stopped). The government comprehended the necessity of additional measures to support construction industry, which in Moscow take the form of a target program to build garages “People’s garage”. The program implementation has attracted cash of citizens and provided the building organizations with construction orders.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.032
GPT teacher head0.206
Teacher spread0.174 · 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 designNot applicable
Domainnot available
GenreOther

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

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