Adapting to environmental hazards in Norway: Confusion over legal responsibility results in stalled action and greater risk of disaster
Bibliographic record
Abstract
In Norway, legal responsibility for adapting to environmental hazards is divided between landowners, municipalities and the national government. However, specific responsibilities often break down in practice. In this short paper, we adopt a legal dogmatic approach, along with a literature review, to better understand legal responsibility for adapting to quick-clay landslides in Norway, which are increasing in occurrence across the country due to climate change. We find that confusion around legal responsibility for adaptation has resulted in the deferring of responsibility and stalling of action. In practice, this has left many landowners at greater risk of disaster. Four key factors contribute to confusion: 1) ambiguity in relevant legislation and policy; 2) limited awareness and legal knowledge among the landowners and municipalities; 3) complexity associated with coordinating the multitude of actors and institutions involved; and 4) lack of capacity to plan for and implement adaptation. We provide interventions that aim to address the contributing factors, in order to facilitate government action on adaptation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".