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Record W3156050456 · doi:10.4324/9781003052821-7

Take a ride into the danger zone?

2021· book-chapter· en· W3156050456 on OpenAlexfundno aff
Gunnhildur Lily Magnúsdóttir

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNorges ForskningsrådUniversity of WestminsterYork University
KeywordsForensic engineeringHistoryGeologyEngineering

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.906
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2021
Admission routes1
Has abstractyes

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