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Record W4224319074 · doi:10.3390/jrfm15050196

A Comparative Analysis of the Economic Sustainability of Cultural Work in the UK since the COVID-19 Pandemic and Examination of Universal Basic Income as a Solution for Cultural Workers

2022· article· en· W4224319074 on OpenAlexvenueno aff
Cécile Doustaly, Vishalakshi Roy

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueUniversity of Warwick
KeywordsPrecarityGovernment (linguistics)Basic incomePolitical sciencePandemicEconomic growthSustainabilityIrishCultural policyThe artsCoronavirus disease 2019 (COVID-19)EconomicsLaw

Abstract

fetched live from OpenAlex

The COVID-19 pandemic and related lockdowns across the world have greatly affected an already vulnerable cultural economy and the structural precarity of many cultural workers. After documenting the impacts of the pandemic in the cultural sector and the effectiveness of governmental responses in the UK and in Europe, the article focuses on the visual arts and explores calls for reforms of the cultural economy. While the UK government’s recovery plan went against the country’s cultural policy tradition due to the plan’s interventionist and financially generous nature, it disproportionally benefitted organisations rather than individuals working in the sector, especially in England. The study, conducted on visual arts workers in the UK, shows that many were unable to access these financial recovery schemes and fell through the cracks of the complex criteria set for these funds. This article informs the current debate on measures that are potentially more economically sustainable and wellbeing protective than those currently in place for cultural workers, such as Universal Basic Income. Its applicability is explored with reference to the historic French and recent Irish examples.

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)0.002
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 score0.025
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.039
GPT teacher head0.309
Teacher spread0.270 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

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