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Record W3163953682 · doi:10.2478/eustu-2022-0045

A Tale of Two Recipes: Well-being Policy Comes to the Western Capitalism Rescue in the (Post-) Trump and (Post-) COVID-19 Era

2020· article· en· W3163953682 on OpenAlexaffabout
Tatjana Muravska, Denis Dyomkin

Bibliographic record

VenueEuropean Studies. The Review of European Law, Economics and Politics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsCarleton University
Fundersnot available
KeywordsCapitalismPolitical economyAuthoritarianismWelfare stateRecessionPolitical scienceAusteritySolidarityPoliticsPopularityState (computer science)Development economicsIdeologyGlobal recessionEconomicsDemocracyLawKeynesian economics

Abstract

fetched live from OpenAlex

Summary The neo-authoritarian “Trump Era”-induced political problems have been aggravated further by the coronavirus that triggered an economic slowdown. The new landscape put Western capitalism, international cooperation, and European integration at risk. This contribution shows similarities and differences in policies dealt with the recessions of 2008 and 2020 on both sides of the Atlantic, with a focus on the EU and Canada. It examines the rising popularity of the welfare state concept applied both to individuals and industries particularly in the EU, for which the protection of citizens’ well-being and solidarity values are at the bloc integration’s core. Ideologically opposing solutions for those crises reflect a fundamental shift in policymaking, reinforcing state interventions policies vs neoliberal approach to the extent of intensified discussions of a universal basic income notion as a response to the inequalities. The article highlights the need for multi – and cross-disciplinary approaches, benefiting policymaking.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.024
Scholarly communication0.0110.013
Open science0.0010.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.049
GPT teacher head0.343
Teacher spread0.293 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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Same venueEuropean Studies. The Review of European Law, Economics and PoliticsSame topicMisinformation and Its ImpactsFrench-language works237,207