Bibliographic record
Abstract
Climate institutions, such as government agencies, are important sites for climate change action. However, the type of action available to any institution will relate to the historical experiences of the institution and those embedded within it. Thus, institutions may exhibit path-dependency, based on previous experiences, such as gender-blindness. This may make the inclusion of gender and other climate-relevant social factors appear less appropriate in comparison to technical and economic solutions. This has direct consequences for the types of climate action undertaken and how climate change is framed, thus often as a scientific, technical problem rather than a societal problem with intersectional dimensions. This chapter focuses upon two climate institutions: the Swedish Environmental Protection Agency and the Swedish Transport Administration. Based on the interview data, it explores how civil servants frame possibilities for institutional action and changes in climate policy-making. It highlights that respondents would alternatively see change resulting from government direction and as something that motivated civil servants could lead on. Respondents often asserted that civil servants should aspire to remain apolitical and serve the will of the democratically elected government. Drawing on an intersectionality-inspired feminist institutionalism and organisational studies literature, we suggest several ways for civil servants to take action without damaging their institutional legitimacy. This will increase the possibility of further intersectional understanding and recognition of climate-relevant social differences in climate policy-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".