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Record W4387112515 · doi:10.5604/01.3001.0053.9003

IDENTIFICATION OF CHANGES CAUSED BY THE COVID-19 PANDEMIC ON THE POLISH LABOUR MARKET

2023· article· en· W4387112515 on OpenAlexaboutno aff
Andrzej Kubisiak

Bibliographic record

VenuePolityka Społeczna · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentPandemicProsperityCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)EconomicsFalling (accident)Supply and demandDemographic economicsIdentification (biology)Economic recoveryDevelopment economicsLabour economicsGeographyEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

The economic crisis caused by the COVID-19 pandemic may have catalysed significant changes in the Polish economy and labour market. During the previous decade, the prosperity of the Polish labour market had clearly improved, as illustrated by data on falling unemployment rates, rising employment and wages. The author's main objective is to identify the key changes caused by the pandemic crisis. To this end, a main hypothesis and three specific hypotheses were adopted, which are based on regularities derived from previous economic crises. The hypotheses were verified by relying on a detailed analysis of international and national databases. Based on the analysis, it has been assessed that a deviation from the labour market trends of the 2010-2019 decade as a result of the economic crisis caused by the COVID-19 pandemic. This was a profound change, but its nature should be assessed as short-term and visible mainly in the second quarter of 2020. During the post-collapse recovery period, which can be dated from Q2 2021 onwards, the trends in the Polish labour market increasingly approximated the pre-pandemic trends, both in terms of supply and demand.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.108
GPT teacher head0.310
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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