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Record W3155210376 · doi:10.3390/jrfm14040181

Polish Culture in the Face of the COVID-19 Pandemic Crisis

2021· article· en· W3155210376 on OpenAlexvenueno aff
Angelika Kantor, Jakub Kubiczek

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)VirtualizationGovernment (linguistics)Face (sociological concept)BusinessPublic relationsEconomic growthPolitical scienceSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Cancellation of the events offered by cultural institutions was caused by the restrictions introduced by the government and, at a critical moment, a national lockdown. The COVID-19 pandemic forced cultural institutions to adapt to the new reality. The aim of this article was to present the impact of the pandemic on the activities of cultural institutions, as well as to identify and systematize the activities of such institutions during the pandemic. The following classification, dividing the activities into three groups, has been proposed: virtualization of existing activities, expansion of activities with additional initiatives, and implementation of corporate social responsibility (CSR) initiatives. The greatest challenge was the virtualization of the existing activities and finding new customer markets. The pandemic has contributed to a significant deterioration in the financial situation of cultural institutions because of the reduced income. Long-term effects on cultural institutions may be difficult to predict and losses may be difficult to rebuild.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of risk and financial managementSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207