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Record W4283768308 · doi:10.18559/978-83-8211-129-3/7

Relationship between cyclical fluctuations in the banking and the services sector in Poland

2022· book-chapter· en· W4283768308 on OpenAlexaboutno aff
Robert Skikiewicz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicPolish socio-economic development
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Business cycleBusiness sectorBusinessAccountingEconomicsStatisticsGeographyEconomyMathematicsMacroeconomics

Abstract

fetched live from OpenAlex

The aim of the paper is to explore the relationship between the business tendency survey indicators for the banking sector and the sections and divisions of the services sector in Poland. In the paper, the results are presented of analyses conducted on the basis of data from the business tendency surveys. The time range of analyses covers the period from the first quarter of 2003 to the first quarter of 2020. The data for the banking sector stem from the survey which is carried out quarterly by the Department of Market Research and Services of the Poznań University of Economics and Business. The data for the eleven sections and two divisions of the services sector (according to the Polish PKD code classification) were obtained from the survey conducted by Statistics Poland on a monthly basis. The monthly data were transformed into quarterly with the use of two formulas. In the paper, the results are presented of cross-correlation analysis, in which the maximum length of lags and leads equal four quarters was adopted.

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)0.003
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.229
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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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