MétaCan
Menu
Back to cohort
Record W4248927237 · doi:10.1108/oxan-db198110

Real GDP growth will be slower in Georgia in 2015

2015· other· en· W4248927237 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2015
Typeother
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsDepreciation (economics)DevaluationCurrencySanctionsRemittanceQuarter (Canadian coin)GeorgianEconomicsForeign exchangeInternational economicsInflation (cosmology)International tradeEconomyBusinessGeographyMonetary economicsPolitical scienceMarket economyCapital formationEconomic growth

Abstract

fetched live from OpenAlex

Subject Worsening economic prospects in 2015. Significance Georgia has already started to be affected by the substantial deterioration in Russia's economic performance and the steep depreciation of the ruble against major currencies, particularly in the final quarter of 2014. Russia became Georgia's third-most-important trading partner in 2014, and remittance flows from Russia are an important source of foreign exchange. Currencies in the Caucasus and Central Asia have faced depreciation pressures, with Turkmenistan forced to devalue its currency on January 1, followed by Azerbaijan on February 21. Impacts Domestic demand will fall this year, as remittance flows weaken. The devaluation of Azerbaijan's manat in late February will have a negative impact on exports, as it is Georgia's top export market. If Western sanctions against Russia are not lifted in July, as expected, this will worsen the outlook for the Georgian economy.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.082
Threshold uncertainty score0.163

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.022
GPT teacher head0.319
Teacher spread0.297 · 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

Explore more

Same venueEmerald expert briefingsSame topicRussia and Soviet political economyFrench-language works237,207