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“Not a Major or Complicated Task”: Activating Dugnad under COVID-19 and the Imagination of Equality in the Norwegian Welfare State

2023· article· en· W4387122642 on OpenAlexvenueno aff
Lena Gross

Bibliographic record

VenueAnthropologica · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianGovernment (linguistics)Welfare stateWelfareInequalityHealth careThe ImaginarySociologyState (computer science)PandemicSocioeconomic statusPolitical scienceEconomic growthCoronavirus disease 2019 (COVID-19)PsychologyPoliticsLawEconomicsMedicinePopulation

Abstract

fetched live from OpenAlex

In Norway, the institution of the welfare state and trust in the government defined the country’s approach to tackling the pandemic. In particular, the government’s strategy to activate the cultural concept of dugnad (voluntary, reciprocal communal work), which relies on an equal standing of all participants, plays into the national imaginary of an egalitarian and just society. However, like in other countries, COVID-19 has put the spotlight on inequalities in access to healthcare, information, adequate housing, and more. Investigating infection measures and their indirect consequences can clarify which values and people are given priority in a crisis and who is seen as belonging to Norwegian society. This article points to the pandemic as a magnifying glass revealing the lack of enough emergency care nurses, physicians, equipment, hospital and psychiatry beds, adequate health literacy efforts and more. Moreover, it magnifies heteronormative and Eurocentric ideas of who makes up a family, compounded by nationalistic notions of who is Norwegian enough to belong. By activating dugnad, politicians transferred their responsibilities as elected leaders to individual citizens, leading to the growth of socioeconomic inequalities and health disparities during the pandemic while also resulting in the poor communication of the long-term and indirect costs of pandemic measures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
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 head0.130
GPT teacher head0.451
Teacher spread0.321 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations5
Published2023
Admission routes1
Has abstractyes

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