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Record W3036063846

Analysis of the quarterly evolution of the Gross Domestic Product

2020· article· ro· W3036063846 on OpenAlexaboutno aff
Constantin Anghelache, Mădălina-Gabriela Anghel, Ștefan Virgil Iacob, Tudor Samson

Bibliographic record

VenueEconomie teoretică şi aplicată · 2020
Typearticle
Languagero
FieldEconomics, Econometrics and Finance
TopicEconomic Analysis and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGross domestic productQuarter (Canadian coin)Gross domestic incomeProduct (mathematics)Gross outputEconomicsAgricultural economicsEconometricsGeographyMathematicsMacroeconomicsProduction (economics)Public economicsGross income
DOInot available

Abstract

fetched live from OpenAlex

Gross Domestic Product is the most complex indicator of the results of a country's economy. The Gross Domestic Product expresses the concrete results obtained in the national economy and depending on its evolution, there are also possibilities for increasing consumption, domestic investment and last but not least the possibility of diversification of the national economy. The analysis of the evolution of the Gross Domestic Product was performed starting from the resources and uses that this indicator had in achieving these results. At the same time, the analysis is performed based on the raw data series, but also on the seasonally adjusted data series. In order to highlight the evolution of the Gross Domestic Product, analyzes were performed for periods of time starting with the year 2000, in order to capture the effects of the economicfinancial crisis, but then also for shorter periods of time, 2017-2019 or 2018-2020. All this was done in order to highlight the evolution over time, but also to shed light on the danger that exists in terms of the negative effect of the health crisis, coronavirus, which will certainly have negative effects. Probably if in the second quarter of 2020 we have a decrease in Gross Domestic Product of 12.3%, compared to the first quarter of 2020, it is not excluded that during the third and fourth semesters of 2020 we will also record non-compliant, inconclusive results, which may result in a double-digit reduction in Gross Domestic Product at the end of the year compared to the previous year 2019. The analysis aimed precisely at highlighting these trends and the way in which the structural analysis on different groups of factors (resources, uses and other macroeconomic indicators, inflation, unemployment, foreign direct investment, domestic investment, etc.), have on the national economy. All should have a beginning to restructure strategies to halt the macroeconomic decline and bring it into a position of stabilization and then resumption of growth.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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