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Record W3096571317 · doi:10.1108/jpbafm-07-2020-0126

“Whatever it takes”: first budgetary responses to the COVID-19 pandemic in France

2020· article· en· W3096571317 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Public Budgeting Accounting & Financial Management · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsYork University
Fundersnot available
KeywordsAusterityGovernment (linguistics)DecentralizationPandemicOriginalityCrisis managementPoliticsValue (mathematics)Political scienceCoronavirus disease 2019 (COVID-19)EconomicsEconomic growthPublic administrationEconomic policyBusinessPublic relationsManagementMedicineMarket economy

Abstract

fetched live from OpenAlex

Purpose This paper highlights the emergency budgetary measures taken by the French government in response to the COVID-19 pandemic health crisis and identifies some of the key political, economic, social and environmental factors and consequences associated with those measures. Design/methodology/approach The authors conduct a thorough analysis of official reports, bills and academic and news articles related to the pandemic management in France. The authors’ analysis covers the period from January 24 to July 31, 2020. Findings Despite previous austerity policies, France faced the health crisis with a very high level of debt, which has complicated the management of the COVID-19 crisis. Although significant, the response brought by the French government seems in the end to be rather choppy. Originality/value This paper highlights three elements of analysis that allow a better understanding of the budgetary management process in France. The authors first discuss the notion of budgetary flexibility. Then, they show that the growth of participatory budgets in local communities gives hope for a possible and much needed decentralization process implying a stronger commitment of citizens. Finally, they highlight a budgetary paradox; that is, massive funding of polluting industries versus ecological issues. These three elements of analysis all advocate the need for a deeper engagement among different levels of government and actors.

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.

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)0.021
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.173
GPT teacher head0.432
Teacher spread0.258 · 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