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Record W3116756270 · doi:10.5539/jpl.v14n2p39

The Impact of Oil Prices on Economic Activity: The Case of Azerbaijan

2020· article· en· W3116756270 on OpenAlexvenueno aff
V. B. Shahin, G. M. Jamila, G. M. Fargana, Nazim Hajiyev

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsOil priceInvestment (military)Econometric modelOil reservesOil-storage tradeGross domestic productSustainable growth rateGovernment (linguistics)Sustainable developmentMacroeconomicsEconomic policyInternational economicsMonetary economicsPetroleumFinancePolitics

Abstract

fetched live from OpenAlex

The strategic purpose of the economic policy of Azerbaijan is to ensure sustainable growth. The external factors including oil prices in the world market and investments have a significant influence on economic activity in Azerbaijan. The relationship between oil prices and gross domestic product has been scrutinized and the sensitivity of macroeconomic indicators to oil prices has been investigated. The dependence of investment activity, including foreign investments on oil prices has been determined. In the research, econometric models have been constructed in the purpose of studying the impact of oil prices on key macroeconomic indicators from the qualitative and quantitative point of view. At the same time, a comparative analysis of oil reserves of Azerbaijan with other oil countries has been conducted. According to the results, the government should determine new and sustainable growth pillars based on risks emerged from oil price, improve economic policy and accelerate the transition to innovative high-tech models of economic development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.263
Teacher spread0.237 · 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
Published2020
Admission routes1
Has abstractyes

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