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Trends in household income and expenditure, and the pace of credit activity in Georgia

2025· article· en· W7084582964 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInterConf · 2025
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsPaceLoanQuarter (Canadian coin)Capital (architecture)Interest rateBusiness cycleBank creditEconomic recoveryNormalization (sociology)

Abstract

fetched live from OpenAlex

Georgia's financial system remains stable and continues to support economic lending. As of April 2025, the banking sector maintains healthy levels of capital and liquidity. Compared to the previous year, the pace of credit activity has gradually normalized, and in April 2025, the annual growth of loans—excluding the exchange rate effect—stood at 15.5%. This growth was largely driven by an increase in business loans. According to data from the first quarter of 2025, the loan-to-GDP ratio remains below the long-term trend. On one hand, strong economic growth recorded in the first quarter and, on the other, the normalization of credit activity have led to a widening of the negative gap between loans and GDP. The committee estimates that, given the current dynamics of credit activity and the gradual normalization of economic growth, this negative gap will close in 2025. Accordingly, at this stage, there is no need to adjust the cyclical component of the countercyclical capital buffer. Commercial banks continue the phased accumulation of the neutral component of the countercyclical capital buffer. In January–May 2025, the volume of new loans issued by commercial banks (24 billion GEL) increased by 7% compared to the same period of the previous year (January–May 2024 – 22.4 billion GEL). For comparison, loan growth in the first five months of 2024 was 18.5%, in 2023 – 17.4%, and in 2022 – 12%. In summary: the growth rate of new loans issued by commercial banks in January–May 2025 has significantly slowed down.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.223

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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