MétaCan
Menu
Back to cohort
Record W4399586711 · doi:10.54694/stat.2023.43

Statistics on Income and Living Conditions (SILC) Survey in the Czech Republic: Methodology and History

2024· article· en· W4399586711 on OpenAlexaboutno aff
Barbora Linhartová Jiřičková, Táňa Dvornáková, Jiří Vopravil

Bibliographic record

VenueStatistika Statistics and Economy Journal · 2024
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsCzechSILCContext (archaeology)Quarter (Canadian coin)Standard of livingGeographyDemographic economicsEconomic growthSocioeconomicsPolitical scienceSociologyEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

EU-SILC is a survey focused mainly on mapping income and living conditions of households. In the Czech Republic, the survey has been conducted annualy since 2005 under the name “Životní podmínky” (Living Conditions). Each year, approximately 10 thousand households are surveyed – around one quarter of these households for the first time, while the rest repeatedly as part of the four-year rotating panel. As the EU-SILC has a uniform methodology for all participating countries, the results for the Czech Republic can be compared with other European countries or with the EU average. The Living Conditions survey was introduced in the context of the Czech Republic´s integration into the EU. However, similar surveys focused on households and their current living situation have been conducted regularly in the former Czechoslovakia since 1956. This article focuses primarily on methodology of SILC, but also offers a brief overview of the living conditions surveys in former Czechoslovakia and in present-day Czech Republic.

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 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)0.012
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.278
GPT teacher head0.413
Teacher spread0.135 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueStatistika Statistics and Economy JournalSame topicdemographic modeling and climate adaptationFrench-language works237,207