Making sense of climate change in central government annual reports and accounts: A comparative case study between the United Kingdom and Norway
Bibliographic record
Abstract
Abstract Taking a sensemaking and accountability perspective, this paper explores how the Norwegian and the United Kingdom (UK) central governments understand climate change in the annual reports and accounts and how this shapes its accountability. Using a thematic analysis, we find that the Norwegian central government makes sense of climate change as a global problem requiring coordinated actions with shared responsibility and accountability to international agreements. In contrast, the UK central government understands climate change as a problem for individual departments and accountability to national guidelines. Sensemaking is enabled primarily in the narratives but also visually in the UK central government. Our research contributes to climate‐related and accounting research by illustrating how central government understands climate change. Theoretically, we extend the literature on sensemaking to the public sector and how sensemaking shapes accountability for climate change.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".