“Not a Major or Complicated Task”: Activating Dugnad under COVID-19 and the Imagination of Equality in the Norwegian Welfare State
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".